©2024 International Monetary Fund SDN/2024/001
IMF Staff Discussion Notes
Research Department
Gen-AI: Artificial Intelligence and the Future of Work
Prepared by Mauro Cazzaniga, Florence Jaumotte, Longji Li, Giovanni Melina, Augustus J. Panton,
Carlo Pizzinelli, Emma Rockall, and Marina M. Tavares*
Authorized for distribution by Pierre-Olivier Gourinchas
January 2024
IMF Staff Discussion Notes (SDNs) showcase policy-related analysis and research being
developed by IMF staff members and are published to elicit comments and to encourage debate.
The views expressed in Staff Discussion Notes are those of the author(s) and do not necessarily
represent the views of the IMF, its Executive Board, or IMF management.
ABSTRACT: Artificial intelligence (AI) has the potential to reshape the global economy, especially in the realm
of labor markets. Advanced economies will experience the benefits and pitfalls of AI sooner than emerging
market and developing economies, largely because their employment structure is focused on cognitiveintensive roles. There are some consistent patterns concerning AI exposure: women and college-educated
individuals are more exposed but also better poised to reap AI benefits, and older workers are potentially less
able to adapt to the new technology. Labor income inequality may increase if the complementarity between AI
and high-income workers is strong, and capital returns will increase wealth inequality. However, if productivity
gains are sufficiently large, income levels could surge for most workers. In this evolving landscape, advanced
economies and more developed emerging market economies need to focus on upgrading regulatory
frameworks and supporting labor reallocation while safeguarding those adversely affected. Emerging market
and developing economies should prioritize the development of digital infrastructure and digital skills.
RECOMMENDED CITATION: Cazzaniga and others. 2024. “Gen-AI: Artificial Intelligence and the Future of
Work.” IMF Staff Discussion Note SDN2024/001, International Monetary Fund, Washington, DC.
ISBN: 979-8-40026-254-8
JEL Classification Numbers: E24, J24, J31, O33, O38
Keywords:
Artificial Intelligence, Labor Market, Job Displacement, Income
Inequality, Advanced Economies, Emerging Market Economies, LowIncome Developing Countries
Author’s E-Mail Address:
mauro98cazzaniga@gmail.com, FJaumotte@imf.org, LLi4@imf.org,
GMelina@imf.org, APanton@imf.org, CPizzinelli@imf.org,
ERockall@stanford.edu, MMendestavares@imf.org

* The authors thank Pierre-Olivier Gourinchas and Antonio Spilimbergo for feedback and guidance and many IMF colleagues for
useful comments. The views expressed herein are those of the authors and should not be attributed to the IMF, its Executive Board,
or its management. Any remaining errors are the responsibility of the authors.
 -
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Contents
Executive Summary __________________________________________________________________ 2
I. Introduction _____________________________________________________________________ 3
II. AI Exposure and Complementarity____________________________________________________ 5
III. Worker Reallocation in the AI-Induced Transformation _________________________________ 11
IV. AI, Productivity, and Inequality _____________________________________________________ 15
V. AI Preparedness _________________________________________________________________ 19
VI. Conclusions and Policy Considerations _____________________________________________ 22
Annex I. Data ______________________________________________________________________ 26
Annex 2. Additional Information on AI Occupational Exposure and Potential Complementarity __ 28
Annex 3. Methodology for the Worker Transition Analysis _________________________________ 29
Annex 4. Model Details ______________________________________________________________ 32
Annex 5. AI Preparedness Index ______________________________________________________ 34
References ________________________________________________________________________ 36
Boxes
1. AI Occupational Exposure and Potential Complementarity1 _________________________________ 24
2. Artificial-Intelligence-led Innovation and the Potential for Greater Inclusion1 ____________________ 25
Figures
1. Employment Shares by AI Exposure and Complementarity: Country Groups and Select ___________ 8
2. Employment Share by Exposure and Complementarity (Selected Countries) _____________________ 9
3. Share of Employment in High-Exposure Occupations by Demographic Groups __________________ 10
4. Share of Employment in High-Exposure Occupations by Income Deciles _______________________ 11
5. Occupational Transitions for College-Educated High-Exposure Workers for BRA and GBR ________ 12
6. Life Cycle Profiles of Employment Shares by Education Level for Brazil and the United ___________ 13
7. 1-Year Re-Employment Probability of Separated Workers __________________________________ 14
8. Estimated Wage Premia from Occupation Changes _______________________________________ 15
9. Exposure to AI and to Automation and Income in the UK ___________________________________ 17
10. Change in Total Income by Income Percentile __________________________________________ 18
11. Impact on Aggregates (Percentage ___________________________________________________ 18
12. AI Preparedness Index and _________________________________________________________ 20
13. ICT Employment Share and Individual Components of the AI Preparedness Index ______________ 21
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Executive Summary
Artificial intelligence (AI) is set to profoundly change the global economy, with some commentators
seeing it as akin to a new industrial revolution. Its consequences for economies and societies remain hard
to foresee. This is especially evident in the context of labor markets, where AI promises to increase productivity
while threatening to replace humans in some jobs and to complement them in others.
Almost 40 percent of global employment is exposed to AI, with advanced economies at greater risk but
also better poised to exploit AI benefits than emerging market and developing economies. In advanced
economies, about 60 percent of jobs are exposed to AI, due to prevalence of cognitive-task-oriented jobs. A
new measure of potential AI complementarity suggests that, of these, about half may be negatively affected by
AI, while the rest could benefit from enhanced productivity through AI integration. Overall exposure is 40
percent in emerging market economies and 26 percent in low-income countries. Although many emerging
market and developing economies may experience less immediate AI-related disruptions, they are also less
ready to seize AI’s advantages. This could exacerbate the digital divide and cross-country income disparity.
AI will affect income and wealth inequality. Unlike previous waves of automation, which had the strongest
effect on middle-skilled workers, AI displacement risks extend to higher-wage earners. However, potential AI
complementarity is positively correlated with income. Hence, the effect on labor income inequality depends
largely on the extent to which AI displaces or complements high-income workers. Model simulations suggest
that, with high complementarity, higher-wage earners can expect a more-than-proportional increase in their
labor income, leading to an increase in labor income inequality. This would amplify the increase in income and
wealth inequality that results from enhanced capital returns that accrue to high earners. Countries’ choices
regarding the definition of AI property rights, as well as redistributive and other fiscal policies, will ultimately
shape its impact on income and wealth distribution.
The gains in productivity, if strong, could result in higher growth and higher incomes for most workers.
Owing to capital deepening and a productivity surge, AI adoption is expected to boost total income. If AI
strongly complements human labor in certain occupations and the productivity gains are sufficiently large,
higher growth and labor demand could more than compensate for the partial replacement of labor tasks by AI,
and incomes could increase along most of the income distribution.
College-educated workers are better prepared to move from jobs at risk of displacement to highcomplementarity jobs; older workers may be more vulnerable to the AI-driven transformation. In the UK
and Brazil, for instance, college-educated individuals historically moved more easily from jobs now assessed to
have high displacement potential to those with high complementarity. In contrast, workers without
postsecondary education show reduced mobility. Younger workers who are adaptable and familiar with new
technologies may also be better able to leverage the new opportunities. In contrast, older workers may struggle
with reemployment, adapting to technology, mobility, and training for new job skills.
To harness AI's potential fully, priorities depend on countries’ development levels. A novel AI
preparedness index shows that advanced and more developed emerging market economies should invest in AI
innovation and integration, while advancing adequate regulatory frameworks to optimize benefits from
increased AI use. For less prepared emerging market and developing economies, foundational infrastructural
development and building a digitally skilled labor force are paramount. For all economies, social safety nets
and retraining for AI-susceptible workers are crucial to ensure inclusivity. 
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I. Introduction
Artificial intelligence (AI) promises to boost productivity and growth, but its impact on economies and
societies is uncertain, varying by job roles and sectors, with the potential to amplify disparities. As a
positive productivity shock, AI will expand economies’ production frontiers and will lead to reallocations
between labor and capital while triggering potentially profound changes in many jobs and sectors. AI offers
unprecedented opportunities for solving complex problems and improving the accuracy of predictions,
enhancing decision-making, boosting economic growth, and improving lives. However, precisely because of its
vast and flexible applicability in numerous domains, the implications for economies and societies are uncertain
(Ilzetzki and Jain 2023).
AI represents a wide spectrum of technologies designed to enable machines to perceive, interpret, act,
and learn with the intent to emulate human cognitive abilities. Across this spectrum, generative AI (GenAI)
includes systems such as sophisticated large language models that can create new content, ranging from text
to images, by learning from extensive training data. Other AI models, in contrast, are more specialized,
focusing on discrete tasks such as pattern identification. Meanwhile, automation is characterized by its focus on
optimizing repetitive tasks to boost productivity, rather than producing new content. The field of AI is
experiencing a swift evolution, especially with the advent of GenAI, which has broadened AI's potential
applications. This suggests that its impact will expand to reshape job functions and the division of labor.
One critical dimension to consider is the societal acceptability of AI. Acceptability may vary depending on
job roles. Some professions may seamlessly integrate AI tools, while others could face resistance because of
cultural, ethical, or operational concerns. This uncertainty becomes especially pronounced in labor markets.
Although AI holds the potential for production-oriented applications, its effect will likely be mixed. In some
sectors where human oversight of AI is necessary, it could amplify worker productivity and labor demand.
Conversely, in other sectors, AI might pave the way for significant job displacements. A rise in aggregate
productivity of the economy could however strengthen overall economic demand, potentially creating more job
opportunities for most workers in a ripple effect. Moreover, this evolution could also lead to the emergence of
new sectors and job roles—and the disappearance of others—transcending mere intersectoral reallocation.
Beyond immediate job effects, another critical economic dimension is the capital income channel. As AI
drives efficiency and innovations, those who own AI technologies or have stakes in AI-driven industries may
experience increased capital income. This shift could potentially exacerbate inequalities.
AI challenges the belief that technology affects mainly middle and, in some cases, low-skill jobs: its
advanced algorithms can now augment or replace high-skill roles previously thought immune to
automation. While historical waves of automation and the integration of information technology affected
predominantly routine tasks, AI's capabilities extend to cognitive functions, enabling it to process vast amounts
of data, recognize patterns, and make decisions. As a result, even high-skill occupations, which were
previously considered immune to automation because of their complexity and reliance on deep expertise now
face potential disruption.1 Jobs that require nuanced judgment, creative problem-solving, or intricate data

1 Another historical example of technology that hit the relatively educated is the introduction of the calculator. Before the widespread
use of calculators, the role of accountants was considered a medium- to high-skill job, given that a significant portion of the
population was uneducated. The introduction of calculators led to a reduction in the number of accountants (Wootton and Kemmerer
2007). 
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interpretation—traditionally the domain of highly educated professionals—may now be augmented or even
replaced by advanced AI algorithms, potentially exacerbating inequality across and within occupations. This
shift challenges the conventional wisdom that technological advances threaten primarily lower-skill jobs and
points to a broader and deeper transformation of the labor market than by previous technological revolutions.
The impact of AI is also likely to differ significantly across countries at different levels of development
or with different economic structures. Advanced economies, with their mature industries and service-driven
economies, typically have a higher concentration of jobs in sectors that require complex cognitive tasks. These
economies are therefore both more susceptible to, yet better positioned to benefit from, AI innovations.
Conversely, emerging market and developing economies, often still reliant on manual labor and traditional
industries, may initially face fewer AI-induced disruptions. However, these economies may also miss out on
early AI-driven productivity gains, given their lack of infrastructure and a skilled workforce. Over time, the AI
divide could exacerbate existing economic disparities, with advanced economies harnessing AI for competitive
advantage while emerging market and developing economies grapple with integrating AI into their growth
models.
To inform the discussion on the potential impact of AI on the future of work and which policies
countries should enact in response, this note aims to answer six questions.
(1) Which countries are more exposed to AI adoption? Which countries are likely to benefit most?
(2) How differently will AI affect workers within countries? Which segments of workers are likely to thrive
and which face more risks?
(3) Historically, how frequently did workers shift between roles now facing varying AI exposure? What
insights do these shifts reveal about labor adaptability?
(4) In what ways could AI reshape income and wealth inequality?
(5) What is the potential impact for growth and productivity?
(6) Which countries appear better prepared for the AI transition? How can policies maximize gains and
mitigate likely AI-related challenges?
This note builds on a growing body of work that explores the impact of AI on labor markets and the
macroeconomy. Many empirical studies so far have focused largely on the US, finding that many of the tasks
of a significant portion of the workforce, including those of high-skilled workers, could be substantially replaced
by AI (for example, Felten, Raj, and Seamans 2021, 2023; Eloundou and others 2023; Webb 2020). A few
studies (OECD 2023; Albanesi and others 2023; Briggs and Kodnani 2023) adopt a cross-country approach;
Gmyrek, Berg, and Bescond (2023) undertake a comprehensive review of emerging market economies and
find less exposure to AI than in advanced economies; Colombo, Mercorio, and Mezzanzanica (2019) focus on
the Italian labor market. These studies apply empirical approaches similar to those used in the automation
literature (for example, Autor and Dorn 2013, Acemoglu and Restrepo 2022, Das and Hilgenstock 2022).
This note contributes to the existing literature in four significant ways. First, while previous AI exposure
measures often implicitly equate exposure with substitutability of human tasks, this note attempts to assess the
potential for complementarity and substitution with labor, using the approach developed by Pizzinelli and others
(2023). This method considers the wider social, ethical, and physical context of occupations, along with
required skill levels, to discern whether AI may complement or replace roles. This adds to recent studies that
have attempted to make this distinction using a purely task-based framework (Acemoglu and Restrepo 2018,
2022; Gmyrek, Bert, and Bescond 2023). Second, the note offers some initial insight into the potential for 
STAFF DISCUSSION NOTES Gen-AI: Artificial Intelligence and the Future of Work
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workers to make the transition from occupations at risk of displacement to those with high AI-complementarity
potential, drawing on microdata for one advanced and one emerging market economy. Third, it takes a deep
look at how AI may affect income and wealth inequality within countries. It dissects AI exposure patterns across
demographics and earnings levels and uses a model-based analysis to evaluate AI's impact on labor and
capital income inequality, as well as on income levels. Last, the note examines how AI preparedness for this
technological shift may differ across countries at different income levels, using a very large sample of advanced
and emerging market and developing economies.
With this analysis there are some important caveats. First, although in the model analysis activity grows in
occupations with high AI complementarity and falls in low-complementarity occupations—mimicking sectoral
reallocations—the analysis on AI exposure assumes that sector sizes are fixed and that the tasks required in
each occupation are unchanged. Consequently, the results are more pertinent for the short to medium term.
Over longer horizons, workers will likely migrate across different sectors and roles, or acquire new skills, and
jobs will evolve. In addition, the analysis assumes that workers within the same occupation will be affected in
the same way, but there can be variation in the effects of AI. AI may also affect firm dynamics and market
concentration (Babina and others, forthcoming), driving inequality between workers at different firms. Second,
the study relies on the premise that tasks performed within similar occupations are homogenous around the
world, while there can be significant cross-country variations. Third, the approach abstracts from linkages
across occupations and countries (trade linkages), as well as from cross-border spillovers of AI exposure. Last,
while the analyses on workers’ AI exposure and societies’ preparedness use empirical approaches, the
potential impacts on inequality and productivity are analyzed with a model. The latter therefore depend on
potentially strong calibration assumptions. The pace of AI adoption, influenced by the time needed by firms to
invest in any necessary physical capital and the reorganization required to capitalize on AI, is difficult to
foresee. Likewise, the time required to exert aggregate macroeconomic effects, the impact on intersectoral
reallocation of factors for production, the birth of new industries, and AI’s exact implications for economies and
societies are challenging to predict. Any estimate embodies a level of uncertainty reminiscent of past
introductions of general-purpose technologies, such as electricity. This uncertainty applies also to the results of
this note.
The remainder of the note is structured as follows. Section II illustrates the conceptual framework of AI
exposure and complementarity and attempts to quantify empirically the degree of exposure to and
complementarity with AI across countries and groups of workers within countries. Section III examines how
easily workers have historically shifted across roles now facing varying degrees of AI exposure and
complementarity. Section IV uses a model to project potential implications of AI adoption for productivity,
incomes, and inequality. Section V assesses countries’ AI preparedness in key policy areas. Section VI
concludes and presents policy considerations.
II. AI Exposure and Complementarity
II.1 Conceptual Framework
Assessing the impact of AI on employment is complex because of its swift evolution, uncertainty in
integration across production processes, and shifting societal perceptions. Given the rapid advance and
evolving capabilities of AI-based technologies, which production processes will integrate AI and which human 
STAFF DISCUSSION NOTES Gen-AI: Artificial Intelligence and the Future of Work
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tasks will be replaced or enhanced remain uncertain. Over time, the changing social acceptability of AI could
also affect its integration into production processes.
This note refines a commonly used conceptual framework to better measure human work’s exposure
to, and complementarity with, AI. To study the effect of technological innovation on jobs, it is standard to
conceptualize individual occupations as a bundle of tasks and to consider which tasks can be replaced or
complemented by technology (see for instance Acemoglu and Restrepo 2022; and Moll, Rachel, and Restrepo
2022 for recent applications). Felten, Raj, and Seamans (2021, 2023) define “exposure” to AI as the degree of
overlap between AI applications and required human abilities in each occupation. The analysis refines this
approach by augmenting it with Pizzinelli and others’ (2023) index of potential AI complementarity. This index
leverages information on the social, ethical, and physical context of occupations, along with required skill levels
(see Box 1 for details). The index reflects an occupation’s likely degree of shielding from AI-driven job
displacement and, when paired with high AI exposure, gives an indication of AI complementarity potential. For
example, because of advances in textual analysis, judges are highly exposed to AI, but they are also highly
shielded from displacement because society is currently unlikely to delegate judicial rulings to unsupervised AI.
Consequently, AI will likely complement judges, increasing their productivity rather than replacing them.2
Conversely, clerical workers, who are also very exposed to AI but have a lower level of shielding, are more at
risk of being displaced. The level of shielding and complementarity will likely evolve over time and at a different
pace across countries, reflecting higher AI accuracy, which will decrease the chances for “hallucinations”—AI
system output that is not based on reality or a given context. Social preferences and available alternatives will
also play a role (see Pizzinelli and others 2023 for quantitative illustrations of this phenomenon). For example,
in low-income countries, where trained doctors are scarce, scalable AI-backed medical consultations may be
viewed as an attractive option. The remainder of this note refers to the complementarity potential driven by high
AI exposure and high shielding more succinctly as “complementarity.”
Joint consideration of exposure and complementarity indicates the types of labor market
developments each occupation is more likely to experience with AI adoption. Occupations with high
exposure for which AI can autonomously complete tasks may see reduced human labor demand, leading to
lower wages. Jobs that require human supervision over AI may experience a boost in productivity, which would
raise labor demand and wages for incumbent workers. However, even in occupations in which AI is likely to
complement human labor, workers without AI-related skills risk reduced employment. Hence, the ease of
acquiring AI-related skills will determine the ultimate impact of this technology.
Based on these two criteria, occupations can be categorized into three groups: “high exposure, high
complementarity”; “high exposure, low complementarity”; and “low exposure” (see Box 1).3
 Although
the indicators (and the thresholds adopted to define what is high and low, represented by their median values)
are relative measures, this categorization highlights the overarching differences across occupations in terms of
their AI exposure and complementarity potential. High-exposure, high-complementarity occupations have
significant potential for AI support, as AI can complement workers in their tasks and decision-making. However,
there is limited scope for unsupervised use of AI in these roles. These are primarily cognitive jobs with a high
degree of responsibility and interpersonal interactions, such as those performed by surgeons, lawyers, and

2 One caveat is the possibility that increased productivity for certain high-exposure, high-complementarity jobs may lead to a decline
in their demand.
3 As discussed in Box 1, complementarity is of limited relevance when AI exposure is limited. Hence, for the sake of simplicity, this
note groups occupations with low exposure together regardless of their potential complementarity. 
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judges. In such roles, workers can potentially reap the productivity benefits from AI, provided they have the
skills needed to interact with the technology. On the other hand, high-exposure, low-complementarity
occupations are well positioned for AI integration, but there is a greater likelihood that AI will replace human
tasks. This could lead to a decline in labor demand and slower wage growth for these jobs. Telemarketers are
a prime example. Last, low-exposure occupations” have minimal or no potential for AI application. This group
encompasses a diverse range of professions, from dishwashers and performers to others.
This conceptual framework is subject to several caveats. First, the index of Felten, Raj, and Seamans
(2021) and the complementarity measure discussed in Box 1 offer only a relative interpretation. In other words,
these measures tell us whether a given occupation is more or less exposed, or complementary, than others.
Second, high complementarity can still result in displacement from occupations of workers who do not have the
required skills or whose employers do not invest in the technology. Companies investing in these technologies
earlier would solidify commercial advantages over competitors. In other words, while the analysis assumes that
workers within the same occupation will be affected in the same way, there can be variation in the effects of AI.
Firms that are more successful at integrating AI may increase their productivity more than competitors and pay
higher wages, exacerbating intra-occupational inequality. Third, the conceptual framework provides only a
static view of exposure and complementarity. In this regard, it does not speak to the existing or prospective
availability of necessary IT infrastructure or to workers’ ability to acquire the needed skills or to relocate across
different occupations. Neither does it take into account the effects of ongoing integration of AI and robotics. In
addition, it does not factor in potential changes in societal preferences, which will also shape regulations and
could make unsupervised AI acceptable in a growing number of contexts or ban its use in others. On the
macroeconomic side, it does not account for adoption speed and the factors influencing adoption, including
costs borne by firms compared with productivity benefits. The conceptual framework also does not factor in
feedback effects, which, for example—through higher overall productivity as a result of AI adoption—could
boost labor demand for most types of jobs, partially offsetting potential negative impacts of AI.
The note applies this categorization to appraise the exposure of the current employment structure to AI
for a large number of countries. The definitions are applied to 142 countries using the online International
Labour Organization (ILO) employment database and an internationally consistent classification of occupations.
To examine within-country variation, a more granular level of the categorization—based on more than 400
occupation titles—is also applied to countries with good microdata coverage: two advanced economies (UK
and US) and four emerging market economies (Brazil, Colombia, India, South Africa).4

II.2 Cross-Country Differences
About 40 percent of workers worldwide are in high-exposure occupations; the share is 60 percent in
advanced economies, which indicates potentially large macroeconomic implications. Advanced
economies have a greater share of high-exposure occupations, with either low or high complementarity, than
emerging market economies and low-income countries (Figure 1, panel 1). In the average advanced economy,
27 percent of employment is in high-exposure, high-complementarity occupations, 33 percent in high-exposure,
low-complementarity jobs. In comparison, emerging market economies have corresponding shares of 16 and

4 Specifically, the analysis of the 142 countries from the ILO database uses 72 sub-major occupation groups (2-digit level) of the
International Standard Classification of Occupations (ISCO)-08 classification. The microdata analysis uses the 130 minor groups (3-
digit) of the same classification for India and the 436 unit groups (4-digit) for the other five countries. See Annex 1 for details. 
STAFF DISCUSSION NOTES Gen-AI: Artificial Intelligence and the Future of Work
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24 percent, respectively, and low-income countries have shares of 8 and 18 percent, respectively.5 A similar
result emerges when looking at selected individual countries using more refined classifications (Figure 1, panel
2). Almost 70 and 60 percent of UK and US employment, respectively, is in high-exposure occupations,
approximately equally distributed between those that are high- and low-complementarity positions. Highexposure employment in emerging market economies ranges from 41 percent in Brazil to 26 percent in India.
Figure 1. Employment Shares by AI Exposure and Complementarity: Country Groups and Selected
Individual Countries
1. Country Groups
(Percent)
2. Selected Countries
(Percent)
Sources: American Community Survey; Gran Encuesta Integrada de Hogares; India Periodic Labour Force Survey; International Labour
Organization; Labour Market Dynamics in South Africa; Pesquisa Nacional por Amostra de Domicílios Contínua; UK Labour Force Survey; and IMF
staff calculations.
Note: Country labels use International Organization for Standardization (ISO) country codes. AEs = advanced economies; EMs = emerging market
economies; LICs = low-income countries; World = all countries in the sample. Share of employment within each country group is calculated as the
working-age-population-weighted average.
The composition of the labor force in terms of broad occupational groups reflecting countries’
economic structure explains most of the differences in exposure and complementarity across
countries. Figure 2 reports the employment shares by occupational groups for three countries with markedly
different shares of employment in exposed occupations. The UK has a significant portion of employment in
professional and managerial occupations, which exhibit high exposure and high complementarity, and in
clerical support workers and technician occupations, generally high exposure and low complementarity. In India
most workers are craftspeople, skilled agricultural workers, and low-skilled, or “elementary” workers; most of
these are in the low-exposure category. Brazil represents a broadly intermediate case.

5 There is heterogeneity behind average figures. In advanced economies the share of employment in high-exposure, highcomplementarity occupations (HEHCs) ranges between 20.2 and 37.3 percent; the share in high-exposure, low-complementarity
occupations (HELCs) ranges between 25.9 and 46.1 percent; and the share in low-exposure occupations (LEs) ranges between
22.5 and 53.6 percent. In emerging market economies, the ranges are 5.7–28.2 percent for HEHCs, 10.4–34.7 percent for HELCs,
and 46.1–75.9 percent for LEs. In low-income countries, the ranges are 2–35.3 percent for HEHCs, 1.4–33 percent for HELCs, and
54–96.1 percent for LEs.
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Figure 2. Employment Share by Exposure and Complementarity (Selected Countries)
1. Brazil
(Percent)
2. United Kingdom
(Percent)
3. India
(Percent)
Sources: India Periodic Labour Force Survey; Pesquisa Nacional por Amostra de Domicílios Contínua; UK Labour Force Survey; and IMF staff calculations.
Note: The charts plot the total employment share by each of the nine 1-digit International Standard Classification of Occupations (ISCO)-08 occupation
codes.
These findings suggest that advanced economies may be more susceptible to labor market shifts from
AI adoption, materializing over a shorter time horizon than in emerging market economies and lowincome countries. Given their high shares of employment in both low- and high-complementarity occupations,
advanced economies may experience a more polarized effect from the structural transformation brought about
by AI. On one hand, they face a greater risk of labor displacement and harmful income developments for
workers in the high-exposure and low-complementarity occupations. On the other hand, they are better
positioned to take advantage early of the emerging AI growth opportunities as a result of their larger amount of
employment in high-exposure and high-complementarity jobs. The net employment impact will depend on
countries’ ability to innovate, adopt, and adapt to AI. Both advanced and emerging market and developing
economies are subject to considerable uncertainty surrounding these predictions. For example, in low-income
countries AI adoption could mirror the swift adoption of mobile technology and lead to large marginal benefits
from AI. In addition, with the appropriate digital infrastructure in place, AI may also represent an opportunity for
emerging market and developing economies to address skill shortages, especially in the health and education
sectors, potentially increasing inclusion and productivity (Box 2).
II.3 Within-Country Differences
Beyond the overall exposure of each country to AI, different groups within countries are likely to be
affected differently. The advent of AI could exacerbate inequality within countries along various dimensions,
such as the income level of individuals, their education level, or their gender. Understanding which groups are
most vulnerable is essential to design policies that can mitigate those effects. Interestingly, while the overall
exposure of countries to AI differs significantly between advanced and emerging market and developing
economies, the patterns of exposure across individuals within countries are very similar for the two advanced
economies and the four emerging market economies included in the granular microdata analysis. An important
caveat is that findings may be different in other countries.
Exposure is higher for women and for more educated workers but is mitigated by a higher potential for
complementarity with AI (Figure 3). In most countries, women tend to be employed in high-exposure
occupations more than men (Figure 3, panel 1). Because this share is distributed approximately equally 
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between low- and high-complementarity jobs, the result can be interpreted to mean that women face both
greater risks and greater opportunities. Exceptions to this pattern may be attributed to high shares of women in
agricultural jobs, especially in countries where the farming sector is large (for example, India). Turning to
education, in all countries examined, higher education levels are associated with a greater share of
employment in high-exposure occupations, but this is especially pronounced in occupations with high
complementarity (Figure 3, panel 2). The higher level of exposure supports the popular view that, unlike
automation, AI could more strongly affect high-skilled workers. However, higher exposure is alleviated by
greater potential for complementarity. Last, age differences do not exhibit a common pattern (Figure 3, panel
3). This is because the composition of different age cohorts in terms of gender and education is very distinct
across countries, thus overshadowing age-based differences. In the UK and the US, younger groups have
more college-educated individuals thanks to increased university attendance over the past 30 years; gender
composition of age groups is similar. In emerging market economies and low-income countries, there are fewer
people with higher education, but younger groups have more women thanks to recent rises in female labor
participation.
Figure 3. Share of Employment in High-Exposure Occupations by Demographic Groups
1. By Gender
(Percent)
2. By Education
(Percent)
3. By Age
(Percent)

Sources: American Community Survey; Gran Encuesta Integrada de Hogares; India Periodic Labour Force Survey; Labour Market Dynamics in
South Africa; Pesquisa Nacional por Amostra de Domicílios Contínua; UK Labour Force Survey; and IMF staff calculations.
Note: The bars represent employment shares in high-exposure occupations. In panel 1, employment shares are conditional on each gender
category. In panel 2, employment shares are conditional on each of the four education categories (middle school and below, high school, some
college, college or higher). In panel 3, employment shares are conditional on each of the four age intervals. Country labels use International
Organization for Standardization (ISO) country codes.
Exposure is spread along the labor income distribution, but potential gains from AI are positively
correlated with income. The share of employment in occupations at risk of displacement (high-exposure, lowcomplementarity jobs; Figure 4, panel 1) is broadly similar across income quantiles (with a mildly positive slope
in emerging market economies). This differs from previous waves of automation and information technology
during which risks of displacement were highest for middle-income earners. Consistent with popular discourse,
AI differs from traditional automation by potentially affecting jobs of workers throughout the income distribution.
However, employment in occupations that have a high potential for complementarity with AI (high-exposure,
high-complementarity jobs; Figure 4, panel 2) is more concentrated in the upper-income quantiles. The
correlation between earnings and potential complementarity is consistent with the findings on education level
and is even more pronounced for emerging market economies (Figure 4, panel 3). This suggests that AI’s
gains will likely disproportionately accrue to higher-income earners, especially in countries such as India and, to
a lesser extent, the US, where complementarity steadily rises at the top of the distribution. The phenomenon
will likely be more muted in countries such as the UK, where the increase in complementarity plateaus at the
top. 
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Figure 4. Share of Employment in High-Exposure Occupations and Potential Complementarity by
Income Deciles
1. High-Exposure, LowComplementarity
(Percent)
2. High-Exposure, HighComplementarity
(Percent)
3. Potential Complementarity
Sources: American Community Survey; Gran Encuesta Integrada de Hogares; India Periodic Labour Force Survey; Labour Market Dynamics in
South Africa; Pesquisa Nacional por Amostra de Domicílios Contínua; Pizzinelli and others (2023); UK Labour Force Survey; and IMF staff
calculations.
Note: Panel 1 shows the employment share in jobs with high exposure but low complementarity, and panel 2 presents the employment share in jobs
with high exposure and high complementarity, each categorized by income deciles. Panel 3 shows the potential AI occupational complementarity
from Pizzinelli and others (2023), averaged and grouped by income deciles. Country labels use International Organization for Standardization (ISO)
country codes.
III. Worker Reallocation in the AI-Induced
Transformation
In the long term, workers will adjust to changing skill demands and sector shifts, with some potentially
transitioning to high-AI-complementarity roles and some struggling to adapt. The previous section
provided a static picture of AI exposure based on the current employment composition of countries. Over time,
however, workers are likely to adapt to the evolving labor market. Although the analysis on AI exposure and
complementarity is conducted at the occupational level, it is important to make a distinction between jobs and
workers. AI adoption may destroy some jobs (and displace the associated workers) and create or enhance
others—but whether the incumbents are the ones who can reap the associated benefits is unclear. The
employment effects will likely depend on worker characteristics, which in turn will affect their adaptability.
Historical data suggest that some workers may struggle to adapt to technology-induced shifts in the job
market.6
Historical job transition patterns suggest how workers could adapt. This section analyzes microdata from
Brazil and the UK to examine worker transition across occupations with different current AI exposure and

6 In the US, Cortes, Jaimovich and Siu (2017) found that less-educated young men contributed to the decline in routine manual jobs
since the 1980s, while women with intermediate education led the fall in routine cognitive jobs. These workers often moved to lowwage occupations or nonemployment. Most of the reallocation took place through fewer moves into these occupations from
unemployment and inactivity (Cortes and others 2020), suggesting that automation affected job seekers more than current workers.
In the UK, Dabla-Norris, Pizzinelli, and Rappaport (2023) found that routine job decline affected women without college degrees
differently across ages: older women shifted to higher-paying jobs, while younger ones went to lower-paying manual jobs. 
STAFF DISCUSSION NOTES Gen-AI: Artificial Intelligence and the Future of Work
INTERNATIONAL MONETARY FUND 12
complementarity.7 It explores whether age and education affect transitions8 and how these characteristics
affect incomes. In general, workers switch between similar types of occupations, indicating potentially limited
flexibility in adjusting to evolving labor markets. However, there is a significant fraction of switches across
occupations with different levels of exposure to AI. Analyzing these dynamics can provide suggestive evidence
on possible worker movements following AI adoption and help identify potentially vulnerable groups.
Figure 5. Occupational Transitions for College-Educated Workers in Brazil and the United Kingdom
1. Brazil
(Percent)
2. United Kingdom
(Percent)
Sources: Pesquisa Nacional por Amostra de Domicílios Contínua; UK Labour Force Survey; and IMF staff calculations.
Note: “From” indicates the exposure category of the occupation the individual had in the preceding quarter; “to” indicates the exposure category of
the occupation the worker transitioned to. The share of transitions represents the average share of transitions in the “from” category for collegeeducated workers who go to the “to” category.
Workers with a college education have historically shown a greater ability to transition into what are
now jobs with high AI-complementarity potential. Both college- and non-college-educated workers
frequently change occupations. The average yearly occupation-switching probability is 43.7 percent in Brazil
and 29.8 percent in the UK for college-educated workers and 38 percent and 27 percent for non-collegeeducated workers.9 College-educated individuals working in what are or may become AI-intensive jobs tend to
stay within such environments when they switch jobs, irrespective of AI’s complementarity to their roles (Figure
5). In addition, more than a third of those moving away from low-complementarity jobs shift toward roles with
higher AI complementarity, which demonstrates a potential avenue for job growth. Non-college-educated
workers are predominantly found in low-AI-exposure jobs and are less inclined to move to highcomplementarity positions when they switch from high-exposure, low-complementarity occupations.10

7 Annex 3 provides details on the data used for the analysis, and Cazzaniga and others (forthcoming) describe the methodology
and perform further analysis. The analysis in this section is conducted only for the UK and Brazil because the labor force surveys for
these two countries are structured as rotating panels, which allows for tracking individual workers over time. The analysis, however,
comes with a caveat: cohort effects are not included because of the limited time series dimension of the data.
8 Gender is not directly discussed in this section because the main results presented below for each education group hold for both
males and females separately.
9 These values are broadly in line with other evidence on occupational mobility in advanced economies and emerging markets. For
instance, for the US, Kambourov and Manovskii (2009) estimate a yearly occupation switching rate of 21 percent, while Moscarini
and Vella (2008) estimate a monthly rate of 3.5 percent, equivalent to 34.7 percent annually. Meanwhile, for Brazil, Monsueto,
Moreira Cunha, and da Silva Bichara (2014) estimate a 30 percent occupation switching rate over a period of four months.
10 Industry switches also happen, but the classification of AI exposure and complementarity has not been conducted at the industry
level. While some occupations are industry-specific (for example, doctors typically work in health care), others are more versatile
and can cross into other industries.
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Figure 6. Life-Cycle Profiles of Employment Shares by Education Level, Brazil and the United
Kingdom
1. Brazil
(Percent)

2. United Kingdom
(Percent)
Sources: Pesquisa Nacional por Amostra de Domicílios Contínua; UK Labour Force Survey; and IMF staff calculations.
Note: The panels plot the estimated share of employment by age for each exposure category for college- and non-college-educated workers,
according to the calculations described in Annex 3.
AI adoption poses challenges but represents an opportunity for young college-educated workers’
careers. Figure 6 shows that college-educated workers often transition from low- to high-complementarity jobs
in their 20s and 30s. Their career progression stabilizes by their late 30s to early 50s, when they usually have
reached senior roles and are less inclined to make significant job switches. Although non-college-educated
workers show similar patterns, their progression is less pronounced, and they occupy fewer high-exposure
positions. This suggests that young, educated workers are exposed to both potential labor market disruptions
and opportunities in occupations likely to be affected by AI. On one hand, if low-complementarity positions,
such as clerical jobs, serve as stepping stones toward high-complementarity jobs, a reduction in the demand
for low-complementarity occupations could make young high-skilled workers’ entry into the labor market more
difficult. On the other hand, AI may enable young college-educated workers to become experienced more
quickly as they leverage their familiarity with new technologies to enhance their productivity. With the
introduction of generative AI, the use of AI has itself become much easier. A recent study shows that the
productivity impact of an AI-based conversational assistant was greatest for less experienced and low-skilled
customer support workers; the effect on experienced and highly skilled workers was minimal (Brynjolfsson,
Danielle, and Raymond 2023).
Older workers may be less adaptable and face additional barriers to mobility, as reflected in their lower
likelihood of reemployment after termination. Following job termination, older workers are less likely to
secure new employment within a year than young and prime-age workers (Figure 7). Several factors can
explain this discrepancy. First, older workers’ skills, though once in high demand, may now be obsolete as a
result of rapid technological advances. Moreover, after significant time in a particular location, they may have
geographic and emotional ties, such as to a spouse and children, that discourage them from relocation for new
job opportunities. Financial obligations accumulated over the years might also make them less likely to accept
positions with a pay cut. Last, having invested many years, if not decades, in a particular sector or occupation,
there may be a natural reluctance or even a perceptual barrier to a transition to entirely new roles or industries.
This may reflect a combination of comfort with familiar settings, concern about the learning curve in a new
domain, or perceived age bias. These constraints are likely to be relevant also in the context of AI-induced
disruptions. 
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Figure 7. One-Year Reemployment Probability of Separated Workers
1. Brazil 2. United Kingdom
Sources: Pesquisa Nacional por Amostra de Domicílios Contínua; UK Labour Force Survey; and IMF staff calculations.
Note: The bars show the reemployment probability of workers who have recently (within the previous quarter) moved from employment to
unemployment, which is defined as the share of these workers who are again employed one year later. “From” indicates the exposure category of
the occupation the individual had before being unemployed, while “to” indicates the exposure category of the occupation the worker transitioned to.
“Prime age” refers to workers over 35 and under 55; “old” refers to workers 55 and older.
Historically, older workers have demonstrated less adaptability to technological advances; artificial
intelligence may present a similar challenge for this demographic group. After unemployment, older
workers previously employed in high-exposure and high-complementarity occupations are less likely to find
jobs in the same category of occupation than prime-age workers (Figure 7). This difference in the
reemployment dynamics can reflect technological change, changes in workers’ preferences, and age-related
biases or stereotypes in the hiring processes in high-complementarity and high-exposure occupations.
Technological change may affect older workers through the need to learn new skills. Firms may not find it
beneficial to invest in teaching new skills to workers with a shorter career horizon; older workers may also be
less likely to engage in such training, since the perceived benefit may be limited given the limited remaining
years of employment. This effect can be magnified by the generosity of pension and unemployment insurance
programs.11 These channels align with Braxton and Taska (2023), which finds that technology contributes 45
percent of earnings losses following unemployment. This happens primarily because workers lacking new skills
move to jobs where their existing skills are valued but that garner lower wages.
Occupational switches also affect workers’ incomes. In both the Brazil and the UK, progressing to highexposure, high-complementarity occupations is associated with higher wages (Figure 8).12 Greater access to
these types of jobs could thus be an significant driver of income growth for workers in advanced and emerging
market and developing economies. In Brazil (Figure 8, panel 1), workers switching to low-exposure from highexposure occupations tend to experience a contraction in hourly wages. Hence, such transitions may be
associated with income losses.

11 See for example Yashiro and others (2022), who find that in Finland, older workers in occupations more exposed to digital
technologies are more likely to exit employment each year, and this effect is amplified when the workers can access an extension of
benefits, known as the “unemployment tunnel,” which extends unemployment benefits until retirement.
12 A large amount of literature, starting with Kambourov and Manovskii (2009) finds that occupational mobility is an important driver
of wage growth at the individual level and of wage inequality across workers.
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Figure 8. Estimated Wage Premiums from Changing Occupation
(Percent)
1. Brazil 2. United Kingdom
Sources: Pesquisa Nacional por Amostra de Domicílios Contínua; UK Labour Force Survey; and IMF staff calculations.
Note: “From” indicates the exposure category of the occupation the individual had in the preceding year, while “to” indicates the exposure category
of the occupation the worker transitioned to. The premiums are relative to stayers; that is, they represent the increase or decrease in wages in relation
to workers in the “from” category who did not switch occupations over a year. Wage premiums are calculated according to the regression specification
in Annex 2. 95 percent confidence intervals for the point estimates are shown by whiskers.
In summary, as AI reshapes the labor market, workers will likely adapt to shifting demands, with
outcomes varying by education and age. Young college-educated workers are the most vulnerable yet the
most adaptable, often seesawing between job types. Historical patterns from Brazil and the UK reveal that
high-exposure, high-complementarity roles offer wage premiums, while switching to low-exposure roles might
decrease wages. The tendency for workers of all ages to return to similar roles after unemployment suggests
some labor market inflexibility. The ability to adjust is crucial for navigating AI-induced changes. Last, while the
historical patterns examined in this section are informative, the structural transformation AI adoption will
generate is still uncertain, and no one knows for sure how the labor market as a whole and individual workers
will be able to adjust.
IV. AI, Productivity, and Inequality
In this section, a model-based analysis is used to evaluate the potential impact of AI adoption on the
economy and inequality. This analytical approach serves as a complement to the preceding empirical
findings by examining broader effects on the economy, highlighting three critical channels through which AI
may affect it: (1) labor displacement, (2) complementarity, and (3) productivity gains. These three channels are
essential to gauging the potential impact of AI adoption. First, AI adoption may shift tasks previously performed
by labor to AI capital, leading to a reduction in labor income. Second, AI adoption may increase the importance
of tasks that are not displaced by AI, particularly in occupations with high complementarity between human
labor and AI. This leads to a shift in value added and labor demand toward occupations with high AI
complementarity and away from other occupations. Third, AI adoption may lead to broad-based productivity
gains, boosting investment and increasing overall labor demand, which may offset some of the decline in labor
income caused by AI-induced labor displacement. As a result, the overall impact of AI on income levels and
inequality will depend on the extent to which gains in economic activity generated by AI-induced productivity
compensate for any labor income losses. 
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To understand AI's impact on income levels and income inequality, both labor and capital income
channels must be examined. A task-based model, detailed in Rockall, Pizzinelli, and Tavares (forthcoming),
is developed. The model builds on the work of Drozd, Taschereau-Dumouchel, and Tavares (2022) and Moll,
Rachel, and Restrepo (2022). Agents differ by their labor productivity and asset holdings, offering a rich picture
of the income and wealth distribution. AI is assumed to be adopted at its maximum potential and affects agents
according to their AI exposure and complementarity potential. Within this analytical framework, AI's effect on
income operates primarily through the three channels mentioned above. AI adoption also leads to increases in
the return on capital, raising capital income, which in turn increases wealth and wealth inequality consistently
with the initial distribution of asset holdings.
The model is calibrated to the United Kingdom, a country that is highly exposed to AI adoption.
Workers’ income is divided into three categories: (1) labor income, which can be positively or negatively
exposed to AI depending on its degree of complementarity with workers’ skills; (2) capital income, which
increases with AI adoption; and (3) benefits and other income (government benefits, pensions, and so forth).13
Figure 9, panel 1, shows that high-income workers have a much larger share of capital income than middleand low-income workers, suggesting that this source of income may play a crucial role in determining the
income inequality impact of AI adoption. Middle- and low-income workers’ total income depends more on labor
income. The impact of AI on labor income will vary with workers’ AI exposure and complementarity. In line with
the evidence presented in Section II, Figure 9, panel 2, shows that workers’ exposure to AI increases with their
income. However, workers’ potential complementarity with AI also increases with income, albeit in the case of
the UK, it peaks around the 75th percentile, declining slightly thereafter.
The impact of AI is simulated by building three scenarios, which assume a labor share decline in line
with comparable historical episodes associated with automation. The decrease in the labor share has
historically been associated with routine-biased automation and, to a lesser extent, with increased trade,
growing markups, and declining worker bargaining power resulting from the weakening of labor unions.14
Drawing on the change observed in the UK between 1980 and 2014 as a possible scenario, we assume that
the labor share declines by 5.5 percentage points following the introduction of AI. This impact is spread across
the income distribution, depending on workers’ AI exposure and complementarity, as shown in Figure 9, panel
2. The three scenarios embed the same displacement of labor tasks via the capital deepening effect but are
differentiated by (1) low-complementarity, if AI only mildly increases the demand for high-complementarity
occupations; (2) high-complementarity, if AI strongly supports the demand for high-complementarity
occupations; and (3) high-complementarity and high productivity, if AI strongly complements highcomplementary occupations, as in scenario (2), and further augments the productivity of the economy,
predominantly through workers in high-complementarity occupations. The productivity increase is calibrated to
generate close to a 1.5 percentage point increase in the workers’ average annual productivity growth rate in the
first 10 years after AI adoption. This value is at the lower end of firm-level studies estimating the potential
impact of AI adoption on workers’ productivity (as discussed in Briggs and Kodnani 2023).15

13 While pension benefits are usually classified as ordinary income, pension fund income is classified as capital income. For
simplicity, in Figure 9, panel 1, pension income is lumped together with government benefits and other income.
14 See IMF (2017); Dao, Mitali, and Koczan (2019); and Bergholt, Furlanetto, and Maffei-Faccioli (2022) for factors that may explain
the decline in the labor share.
15 While the analysis presented in this section compares steady-state scenarios, the model would also allow for the study of shortterm dynamics toward the long-term steady state. 
STAFF DISCUSSION NOTES Gen-AI: Artificial Intelligence and the Future of Work
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Figure 9. Exposure to AI and to Automation and Income in the UK
1. Exposure of Income to AI
(British pounds)
2. Exposure and Complementarity by Income
Percentiles
(AI and complementarity index)
Sources: UK Office for National Statistics, Wealth and Assets Survey; and IMF staff calculations.
Note: Panel 1 shows three categories of workers’ income by total income percentiles: (1) wage income; (2) benefits, pensions, and other income;
and (3) capital income (rents and estimated investment income). In panel 2, AI exposure is measured as the share of total hours worked in a job in
the top 30 percent of AI Occupational Exposure scores, from Felten, Raj, and Seamans (2021), weighted by hours worked. This threshold is chosen
to make the analysis comparable with historical episodes of automation. AI complementarity is measured by considering work contexts and skills, as
discussed in Box 1 and in detail in Pizzinelli and others (2023). In the panel, we plot AI exposure and complementarity by total income percentiles.
RHS = right scale.
The impact of AI on labor income inequality depends on the race between the degree of exposure to,
and complementarity with, AI, and its boost to productivity.16 When AI has low complementarity with labor,
AI adoption leads to a decline in labor income inequality (Figure 10) because of the displacement effect. At the
top of the income distribution the displacement effect is larger than the complementarity gains, leading to a
labor income decline at the top. When AI is highly complementary to labor, the complementarity effect becomes
stronger than the displacement effect, particularly in the upper half of the income distribution, leading to a
smaller share of high-income workers negatively affected by AI compared with the low-complementarity case.
The share of workers negatively affected at the top drops from almost 15 percent to less than 5 percent. This
high complementarity also leads to a decline in the labor income of those with less complementary tasks, who
are typically among low-income workers. As a consequence, labor income inequality increases. Last, when the
AI productivity impact is also considered, labor income rises for all workers in the economy, even for the
workers who have low exposure and those with high exposure and low complementarity. The main reason is
that higher productivity leads to higher demand for all factors of production in the economy, leading to
increased labor income. However, labor income inequality rises because the increase is larger for workers with
high AI complementarity.
Unlike labor income inequality, capital income and wealth inequality always increase with AI adoption
(Figure 10). The main reason for the increase in capital income and wealth inequality is that AI leads to labor
displacement and an increase in the demand for AI capital, increasing capital returns and asset holdings' value.
In all scenarios, interest rates increase by almost 0.4 percentage point, with the potential to partially offset the
decline in the natural rate of interest in the UK and advanced economies in general.17 Since in the model, as in

16 Annex 4 discusses two additional hypothetical scenarios that disentangle the importance of exposure and complementarity.
17 The increase in the interest rate is approximately of the same magnitude as the decline in the UK natural rate attributable to
demographics (IMF 2023).
STAFF DISCUSSION NOTES Gen-AI: Artificial Intelligence and the Future of Work
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the data, high-income workers hold a large share of assets, they benefit more from the rise in capital returns.
As a result, in all scenarios, independent of the impact on labor income, the total income of top earners
increases because of capital income gains. These model simulations abstract from possible changes in the
definition of property rights, as well as changes in fiscal and redistributive policies, which can help reshape
distributional outcomes (see, for example, Berg and others 2021, in the context of automation; and Klinova and
Korinek 2021, in the context of AI).
Figure 10. Change in Total Income by Income Percentile
1. Low Complementarity
(Percent)
2. High Complementarity
(Percent)
3. High Complementarity and High
Productivity (Percent)
Source: IMF staff calculations.
Note: The panels show three scenarios from the model: (1) low complementarity, (2) high complementarity, and (3) high complementarity and high
productivity. For all scenarios, the calibrated change in the capital share is the same: 5.5 percentage points, based on the change in the capital share
during 1980–2014. The panels show the change in total income by income percentile, decomposed into the change in labor income in blue and the
change in capital income in orange. For more details on the model see Annex 4. P = percentile.
Under the high-complementarity, high-productivity scenario, the increase in total national income is
largest and benefits all workers, although gains for those at the top are larger. In the first scenario, in
which AI has low complementarity, the use of AI leads to an increase in output of almost 10 percent thanks to a
combination of capital deepening and a small increase
in total factor productivity (Figure 11). When higher
complementarity is considered (second scenario), the AI
impact on output and total factor productivity is similar to
the impact in the low-complementarity scenario because
these scenarios assume the same capital deepening
and capital productivity gains. However, higher
complementarity leads to sectoral reallocation, with
labor demand and economic activity moving from low- to
high-complementarity occupations. Total income levels
of low-income workers decline by 2 percent, while the
gains at the top are almost 8 percent, leading to
approximately the same increase in the level of national
income as in the first scenario and an increase in labor
income inequality. Last, when the productivity impact is
also considered, output increases by 16 percent
between steady states, and total factor productivity
Figure 11. Impact on Aggregates
(Percentage points, left scale; percent, right scale)
 Source: IMF staff calculations.
Note: The figure shows the change in the aggregate wage and
wealth Gini between the initial and final distribution in each scenario,
as well as the change in TFP and output. For more details on the
model see Annex 4. RHS = right scale; TFP = total factor
productivity.
STAFF DISCUSSION NOTES Gen-AI: Artificial Intelligence and the Future of Work
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increases by almost 4 percent. These gains happen primarily in the first 10 years of the transition. Under this
third scenario, despite the increase in labor income inequality, the total income level increases for all workers in
the economy, ranging from 2 percent for low-income workers to almost 14 percent for high-income workers.
In emerging market and developing economies with higher initial inequality, AI could amplify wealth
gaps and reduce wage disparity to a larger extent, but if the exposure to AI is lower and widespread, it
could dampen these effects. An important issue is how model results may change when considering two
aspects pertinent to emerging market and developing economies: (1) higher initial levels of income and wealth
inequality and (2) lower exposure to AI. Simulations suggest that higher initial income and wealth inequality
could exacerbate wealth disparity, because AI-associated gains accrue predominantly to top earners. At the
same time, labor income inequality could decrease to a larger extent because of a higher concentration of AIexposed workers at the top of the income distribution. The final effect, however, depends on the degree of
complementarity, as in the case of advanced economies. In an economy with fewer AI-exposed workers, the
direct impact of AI on both income and wealth distribution may be less pronounced, given that fewer people
stand to benefit from AI.18 Last, AI’s potential to enhance public services, modernize finance, and bolster such
sectors as agriculture and health care could boost inclusion and productivity in emerging market and
developing economies. Although these aspects are outside the scope of the model analysis, they are
discussed in Box 2.
Although the model simulations focus on within-country inequality, AI adoption may also have
significant effects on global economic disparity, driven by potential reshoring of activities to advanced
economies. Such a shift could trigger reallocation of capital and labor from less developed regions, which are
not as prepared to harness AI, toward more technologically advanced and AI-ready countries (Alonso and
others 2022). Call centers located in emerging market economies are a potential example. These could be at
risk of replacement by AI-driven solutions, subsequently leading to their relocation to advanced economies. In
addition to labor reallocation, the increased profitability of firms that adopt AI may generate an influx of capital
from emerging market and developing economies to advanced economies, which could reduce equilibrium
interest rates in advanced economies and exert downward pressure on capital income.19 Clearly, these
dynamics are highly uncertain at this stage. It is also possible that, with sufficient investment, AI may help
emerging market and developing economies leapfrog in certain sectors, facilitating the offshoring of a broader
selection of tasks and thus reducing cross-country inequality.
V. AI Preparedness
Preparedness for AI adoption is essential to harness its potential and mitigate its inherent risks. AI
adoption can result in diverse labor market outcomes across countries, particularly regarding workforce
reallocation and inequality. These likely varied outcomes are intertwined with countries’ structural and
institutional frameworks. A country's level of preparedness plays a pivotal role when it comes to maximizing
AI's benefits while managing downside risks, as historical episodes of technology adoption demonstrate
(Cirera, Comin, and Cruz 2022).

18 An important caveat regards the extent to which wealthy people in emerging market and developing economies have invested in
foreign stocks likely to benefit from AI adoption. If such investment is significant, wealthy individuals may get higher returns on their
foreign capital holdings even if domestic adoption is low,.
19 A multicountry version of the model could investigate this and other relevant issues.
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This section proposes an AI Preparedness Index (AIPI), which covers multiple strategic areas for AI
readiness. Drawing from the literature on the cross-country determinants of technology diffusion (for example,
Keller 2004) and adoption (for example, Nicoletti, Rueden, and Andrews 2020), the index is made up of a
selected set of macro-structural indicators that are relevant for AI adoption. These are organized under four
categories: (1) digital infrastructure, (2) innovation and economic integration, (3) human capital and labor
market policies, and (4) regulation and ethics. Annex 5 contains the full list of subindicators and details on the
index construction methodology.
Although each component of the AIPI is important individually, preparedness for AI-induced structural
transformation will likely rely on the collective performance in all areas. For example, the digital
infrastructure component, a crucial determinant of information and communications technology adoption (for
example, Nicoletti, Rueden, and Andrews 2020) can lay the foundation for the diffusion and localized
applications of AI technology. Nonetheless, such infrastructure would be of limited use absent a skilled
workforce capable of leveraging digital platforms for innovative workplace applications (Bartel, Ichniowski, and
Shaw 2007). Therefore, the human capital and labor market policies element, which incorporates the presence
of social safety nets, assesses the prevalence and inclusive distribution of digital skills within the labor force
and the presence of policies that facilitate labor reallocation while safeguarding those harmed by AI-induced
transitions (Nicoletti, Rueden, and Andrews 2020). Coupled with strong infrastructure, a digitally skilled labor
force is vital for innovation and economic integration (Autor, Levy, and Murnane 2003), which not only fosters
domestic technological development through a vibrant R&D ecosystem but also promotes international trade
and attracts foreign investment and new (AI) technologies (Bloom, Draca, and Van Reenen 2015). Last, the
regulation and ethics dimension evaluates the extent to which the existing legal frameworks are adaptable to
evolving new (digital) business models and the
presence of strong governance for effective
enforcement.
Wealthier economies, including advanced and
some emerging market economies, are generally
better prepared than low-income countries to
adopt AI, although there is considerable variation
across countries (Figure 12). Broadly, advanced
and some emerging market economies are highly
exposed to potential disruptions from AI—amid a
substantial share of employment in highly exposed
occupations. Yet these highly exposed economies,
notably the UK and US, as analyzed in Section II, are
also well positioned to harness the benefits and
mitigate the risks of AI thanks to their strong
preparedness, particularly in digital infrastructure,
human capital, and adaptable regulatory frameworks.
On the other hand, low-income countries, although
relatively less exposed, are underprepared across all
dimensions to harness the benefits of AI. Notably,
weak digital infrastructure and a less digitally skilled
labor force are a concern. These
Figure 12. AI Preparedness Index and
Employment Share in High-Exposure
Occupations
Sources: Fraser Institute; International Labour Organization; International
Telecommunication Union; United Nations; Universal Postal Union; World
Bank; World Economic Forum; and IMF staff calculations.
Note: The plot comprises 125 countries: 32 AEs, 56 EMs, and 37 LICs. The
red reference lines are derived from the median values of the AI
Preparedness Index and high-exposure employment. Exes denote the
average values for each corresponding country group. Circles represent the
average values for each respective country group. AEs = advanced
economies; EMs = emerging market economies; LICs = low-income
countries. Country labels use International Organization for Standardization
(ISO) country codes.
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cross-country differences risk amplifying the existing income gap between rich and poor economies, because
advanced economies expect productivity increases, as shown by the model-based simulations in the previous
section.
Figure 13. Information and Communications Technology Employment Share and Individual
Components of the AI Preparedness Index
1. Digital Infrastructure 2. Human Capital and Labor Market Policies
3. Innovation and Integration 4. Regulation and Ethics
Sources: Fraser Institute; International Labour Organization; International Telecommunication Union; United Nations; Universal Postal Union; World
Bank; World Economic Forum; and IMF staff calculations.
Note: ICT employment refers to people working in the information and communications sector based on ISIC-Rev. 4 classification. 142 countries
are included: 35 AEs, 67 EMs, and 40 LICs. Exes denote the average values for each corresponding country group. Circles represent the average
values for each respective country group. Simple correlation (corr.) is also added for each country group. AEs = advanced economies; EMs =
emerging market economies; ICT = information and communications technology; LICs = low-income countries; ISIC = International Standard
Industrial Classification.
Reform prioritization should align with AI preparedness gaps. In this context, it is useful to distinguish
between foundational AI preparedness—digital infrastructure and human capital that enable workers and firms
to adopt AI—and second-generation preparedness (innovation and legal frameworks). For economies with high
AI exposure and strong foundational AI adoption preparedness (advanced economies and some emerging
market economies), more emphasis should be placed on strengthening their digital innovation capacity and
adapting their legal and ethical frameworks to govern and foster AI advances. Accordingly, improvement in
regulatory frameworks—which are critical for broadening societal trust in AI tools—followed by innovation and
integration, are the AI preparedness dimensions more strongly correlated with the size of the digital sector in
advanced economies (Figure 13, panels 3 and 4). Regulatory frameworks need to mitigate cybersecurity risks
as well (Carriere-Swallow and Haksar 2019; Haksar and others 2021), which increase with widespread use of
AI (Bank of America 2023) and may adversely affect firms’ performance (Jamilov, Rey, and Tahoun 2023).
Where foundational preparedness is weak (low-income countries and some emerging market economies), 
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investment in digital infrastructure and human capital should be prioritized to reap early gains from AI while
paving the way for second-generation preparedness. In other words, while the capacity to innovate and
strengthen regulatory frameworks for digital businesses is crucial in attracting (digital) investments in lowincome countries, these frameworks will be less effective without strong AI infrastructure and a digitally skilled
labor force. In some emerging market economies and low-income countries where foundational preparedness
is not a strong binding constraint, improvement in innovation and regulatory frameworks could catalyze private
investment in digital innovations. The correlations reported in Figure 13 (panels 1 and 2) corroborate these
arguments, with digital Infrastructure and human capital strongly associated with the digital sector size in lowincome countries. With such investments, AI has the potential to improve the delivery of fundamental services
such as education and health care and could perform complex tasks in areas where skilled labor is scarce.
However, considering the costs associated with such investments and the limited fiscal space in many lowincome countries, it would be prudent to focus spending on high-return projects.
VI. Conclusions and Policy Considerations
AI adoption may generate labor market shifts with significant cross-country differences. The exact
implications of AI for economies and societies are challenging to predict, embodying a level of uncertainty
reminiscent of past introductions of general-purpose technologies, such as electricity. This uncertainty is
particularly pronounced in labor markets, where AI offers productivity gains but also poses risks of job
displacements. This note’s findings highlight the significant portion of global employment that is exposed to AI,
with advanced economies generally both more exposed but also better positioned to leverage this technology
than most emerging market and developing economies. This dynamic suggests a potential widening of the
digital divide and global income disparity.
Women and highly educated workers are consistently more exposed to, but also more likely to benefit
from, AI; older workers may be more likely to struggle during this technological transition. Both women,
with their strong presence in the services sector, and highly educated workers, typically employed in cognitiveintensive occupations, face greater AI exposure. Yet both groups also stand to gain the most from its
integration. College-educated and younger people move more easily into high-complementarity jobs; older
workers, however, face challenges in reemployment and adapting to new technologies, mobility, and acquiring
new job skills.
Beyond its impact on income levels, which could increase for most workers, AI will also reshape wealth
and income distribution. Capital deepening and the surge in productivity driven by AI hold the potential to
elevate wage incomes for a broad range of workers and to increase total income. This is more likely if AI
exhibits significant complementarity with human labor in several roles and if the productivity boost is sufficiently
strong. The enhanced economic activity and labor demand spurred by AI could offset the negative
consequences of labor displacement. Unlike previous automation waves, which affected mostly middle-skilled
workers, AI's displacement risks span the entire income spectrum, including high-income earners and skilled
professionals. However, the potential for AI to complement jobs is positively correlated with income levels. As
such, the trajectory of labor income inequality hinges on how well AI complements tasks undertaken by highincome professionals. Model simulations suggest that with strong complementarity, high-wage earners might
experience a disproportionate increase in their earnings, thereby intensifying labor income inequality. This
channel would amplify the increase in income and wealth inequality resulting from enhanced capital returns,
which typically accrue to higher-earning people. These channels abstract from countries’ choices regarding the
definition of AI’s property rights and redistributive policies, which will ultimately shape impacts on income and
wealth distribution. 
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Harnessing the advantages of AI will depend on countries’ preparedness and the ability of workers to
adapt to this new technology. Advanced and some emerging market economies are well positioned to
harness AI thanks to their high exposure and preparedness. Other emerging market economies and lowincome countries may find it difficult to harness potential AI benefits given their inadequate infrastructure, their
workers’ lack of skills, and the absence of institutional frameworks—putting them at risk of competitive
disadvantage. Economic development stages influence preparedness priorities. Advanced and more developed
emerging market economies should launch adequate regulatory frameworks to optimize the benefits of
increased AI use and invest in complementary innovations. Low-income countries and other emerging market
economies should prioritize digital infrastructure and human capital. With such investments, AI could help
alleviate skill shortages, expand the provision of health care and education, and improve productivity and
competitiveness in new sectors.
The potential implications of AI demand a proactive approach from policymakers geared toward
maintaining social cohesion. While long-term productivity gains from AI are likely, during the transition, job
displacement and changes in income distribution could have substantial political economy implications. History
shows that economic pressures can lead to social unrest and demands for political change. Ensuring social
cohesion is paramount. Policies must promote the equitable and ethical integration of AI and train the next
generation of workers in these new technologies; they must also protect and help retrain workers currently at
risk from disruptions. The cross-border nature of AI amplifies its ethical and data security challenges and calls
for international cooperation to ensure responsible use, as recently laid out in the Bletchley Declaration, signed
by 28 countries and the EU. Countries have varying capacity to address these issues, which highlights the
need for harmonized global principles and local legislation. 
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Box 1. Artificial Intelligence Occupational Exposure and Potential
Complementarity
Several studies have proposed definitions of AI exposure at the occupational level. The most common
is the AI Occupational Exposure (AIOE) index of Felten, Raj, and Seamans (2021), measuring the
correspondence between 10 AI applications and 52 human skills. This overlap between AI and human
abilities is then weighted by the degree of importance and complexity of such skills in each job. This index is
interpreted in relative terms and reported as normalized or rescaled between 0 and 1. It is also agnostic
about the implication of exposure for human labor. In other words, it focuses on the relative likelihood of AI’s
integration into the functions of a given job, but it does not consider the likelihood of AI serving as a
complementary technology or subsituting for human labor.
Some studies build on the AIOE measure to attempt to answer this question. Pizzinelli and others
(2023) propose a potential complementarity index to adjust the original AIOE measure. In this
approach, greater potential complementarity reduces exposure. Hence, a higher complementarity-adjusted
AIOE (C-AIOE) more explicitly reflects a higher chance of labor substitution. To develop this index, the
authors use O*NET, the same repository of occupational characterisitcs employed by Felten, Raj, and
Seamans (2021), but draw from two different areas: work contexts and skills. Work contexts include social
and physical aspects of how work in a given occupation is carried out. Using case-by-case judgment, the
authors argue that in some contexts societies may be less likely to allow unsupervised use of AI. For
instance, the criticality of decisions and the gravity of the consequences of errors are two job aspects that
may motivate societies to require humans to make final decisions or take actions. Judges and doctors, for
example, despite high AI exposure, would still likely be human beings.
Conceptually, exposure and complementarity can be
thought of as two dimensions of relevance, as in Box
Figure 1.1. At the first stage, exposure (x-axis) defines the
scope for applying AI to carry out the main functions of a job. At
the second stage, given the degree of potential application, a
set of societal and technical concerns determines
complementarity. For occupations with high exposure, low
complementarity entails a relatively higher likelihood of AI
replacing key tasks. In more acute cases, AI may lead to a
decrease in the demand for the occupation altogether. This
would in turn translate into reduced employment prospects,
lower wages, and higher risk of displacment. High exposure
combined with high complementarity entails a greater likelihood
of workers in those jobs experiencing productivity growth and
wage gains from adopting AI-driven technologies. However,
these benefits will likely be contingent on possessing the skills
needed to use AI. Without such skills, workers may be at a
disadvantage and may experience lower compensation and reduced employment prospects. Last, at lower
levels of exposure, complementarity becomes less relevant, because the tasks in an occupation that are
likely to be either supported or replaced by AI are less integral components of the job itself (see Annex 2 for
additional details).
This box was prepared by Carlo Pizzinelli.
Box Figure 1.1. Conceptual Diagram
of AI Occupational Exposure (AIOE)
and Complementarity (θ)
Sources: Felten, Raj, and Seamans (2021); Pizzinelli
and others (2023); and IMF staff calculations.
Note: Red reference lines denote the median of
AIOE and compementaity. 
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Box 2. Artificial-Intelligence-Led Innovation and the Potential for Greater
Inclusion
Growing AI adoption has the potential to exacerbate cross-country and within-country inequality.
This box argues, however, that there are also several avenues through which AI could be leveraged to foster
inclusion in developing economies. Enhancing inclusion in the delivery of public services that focus on
boosting human capital, such as health care and education, as well as in agriculture and credit access,
presents a promising avenue through which AI can augment productivity.
One example is the transformative role of digitalization in government technology ("govtech").
Historically, digitalization has helped modernize public finance by enhancing revenue collection and
spending efficiency. It has also improved the delivery of social services, thereby fostering inclusion and
reducing inequality (Amaglobeli and others 2023). Notably, during COVID-19–related lockdowns, nations
such as Namibia, Peru, Zambia, and Uganda successfully used their digital infrastructure to expedite the
distribution of financial aid. AI could amplify this wave of transformation by assisting in informed decisionmaking, identifying service gaps, detecting fraud and corruption, and customizing local interventions.
By streamlining bureaucratic tasks, AI tools could also free up time and resources, which could be
better allocated to key sectors for inclusion—for example, agriculture, health care, and education.
Interventions in these sectors benefit primarily the socially and economically vulnerable. In agriculture, AI
could be leveraged to predict yields, optimize irrigation, and identify potential pests, thereby enhancing food
security and productivity (IFC 2020). In health care, AI could assist in predictive analytics to foresee
outbreaks, optimize resource allocation in hospitals, facilitate diagnoses, and make quality health care
accessible and affordable even in areas with shortages of qualified medical staff (Wahl and others 2018;
USAID 2019). In education, personalized learning experiences could be delivered through AI algorithms,
reducing the human capital divide in regions lacking qualified educators (UNESCO 2021).
AI also holds the promise of advancing financial inclusion, specifically by using unconventional data
to evaluate creditworthiness (IFC 2020). This would allow underserved communities to gain access to
financial services that would otherwise be out of reach. Given the risks associated with AI technologies—
such as potential embedded bias and opaque outcomes (Shabsigh and Boukherouaa 2023)—their
deployment should be accompanied by stronger frameworks for monitoring and oversight (Boukherouaa and
others 2021; FCA 2022). The expansion of digital financial services has historically been linked with
increased inclusion. An IMF study (Sahay and Čihák 2020) analyzed 52 emerging market and developing
economies and underscored a marked rise in digital financial inclusion, with notable progress in Africa and
Asia. COVID-19 further accelerated the growth of digital financial services, which tend to benefit low-income
households and small businesses while promoting economic growth and reducing inequality (Sahay and
others 2017; Sahay and Čihák 2020).
While AI adoption promises transformative change, its successful implementation requires substantial
investment, political commitment, and safeguards for data security and privacy.
This box was prepared by Giovanni Melina. 
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Annex I. Data
I.1 Descriptive Charts
Annex Table 1.1. Data Sources for Stylized Facts
Figures Sources Economies
Figure 1. Employment Shares by AI Exposure
and Complementarity: 1. Country Groups ILO 32 AEs, 56 EMs, 37 LICs
Figure 1. Employment Shares by AI Exposure
and Complementarity: 2. Selected Countries
ACS, GEIH, India PLFS,
LMDSA, PNADC, UK LFS
BRA, COL, GBR, IND, USA,
ZAF
Figure 2: Employment Share by Exposure and
Complementarity
India PLFS, PNADC, and UK
LFS BRA, GBR, IND
Figure 3. Share of Employment in HighExposure Occupations by Demographic
Groups
ACS, GEIH, India PLFS,
LMDSA, PNADC, UK LFS
BRA, COL, GBR, IND, USA,
ZAF
Figure 4. Share of Employment in HighExposure Occupations and Potential
Complementarity by Income Deciles
ACS, GEIH, India PLFS,
LMDSA, Pizzinelli and others
(2023), PNADC, and UK LFS
BRA, COL, GBR, IND, USA,
ZAF
Figure 5. Occupational Transitions for CollegeEducated Workers for Brazil and the United
Kingdom
PNADC and UK LFS BRA, and GBR
Figure 7. One-Year Reemployment Probability
of Separated Workers PNADC and UK LFS BRA, GBR
Figure 8: AI and Informality PNADC BRA
Figure 12. AI Preparedness Index and
Employment Share in High-Exposure
Occupations
FI, ILO, ITU, UN, UPU, WB,
WEF
32 AEs, 56 EMs, 37 LICs
Figure 13. Information and Communications
Technology Employment Share and Individual
Components of the AI Preparedness Index
FI, ILO, ITU, UN, UPU, WB,
WEF
35 AEs, 67 EMs, 40 LICs
Box Figure 1.1: Conceptual Diagram of AI
Occupational Exposure (AIOE) and
Complementarity (θ)
Felten, Raj, and Seamans
(2021), Pizzinelli and others
(2023)
Source: IMF staff.
Note: Survey year considered: 2019 for USA, ZAF, IND; 2022 for COL, GBR, BRA. Regarding survey sample size, 2,239,553 for
USA, 238,251 for GBR, 1,923,188 for BRA, 919,459 for COL, 69,420 for ZAF, 420,720 for IND. American Community Survey
(ACS); Gran Encuesta Integrada de Hogares (GEIH); India Periodic Labour Force Survey (PLFS); International Labour
Organization (ILO); Labour Market Dynamics in South Africa (LMDSA); Pesquisa Nacional por Amostra de Domicílios Contínua
(PNADC); UK Labour Force Survey (LFS). AEs = advanced economics; EMs = emerging markets; LICs = low-income countries.
Country names use International Organization for Standardization (ISO) country codes. 
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I.2 Country Coverage
Annex Table 1.2. Country Sample Coverage
ISO3 Country Income
Group ISO3 Country Income
Group ISO3 Country Income
Group
SSD South Sudan LIC BOL Bolivia EM GEO Georgia EM
AFG Afghanistan LIC IRN Iran EM SYC Seychelles EM
CAF Central African Republic LIC PRI Puerto Rico AE MEX Mexico EM
SOM Somalia LIC BGD Bangladesh LIC OMN Oman EM
MRT Mauritania LIC SLV El Salvador EM QAT Qatar EM
SDN Sudan LIC GTM Guatemala EM THA Thailand EM
TCD Chad LIC EGY Egypt EM SRB Serbia EM
LBY Libya EM SEN Senegal LIC CRI Costa Rica EM
COD Congo, Democratic Republic of the LIC MAC Macao SAR AE TUR Türkiye EM
STP São Tomé and Príncipe LIC PRY Paraguay EM URY Uruguay EM
YEM Yemen LIC BWA Botswana EM KAZ Kazakhstan EM
ETH Ethiopia LIC LBN Lebanon EM RUS Russia EM
COM Comoros LIC SUR Suriname EM HUN Hungary EM
MOZ Mozambique LIC NAM Namibia EM SAU Saudi Arabia EM
AGO Angola EM BLZ Belize EM BGR Bulgaria EM
GNB Guinea-Bissau LIC GUY Guyana EM HRV Croatia AE
HTI Haiti LIC GHA Ghana LIC GRC Greece AE
IRQ Iraq EM KGZ Kyrgyz Republic LIC ROU Romania EM
VEN Venezuela EM TLS Timor-Leste LIC CHL Chile EM
COG Congo, Republic of LIC BIH Bosnia and Herzegovina EM SVK Slovak Republic AE
PNG Papua New Guinea LIC MAR Morocco EM POL Poland EM
BDI Burundi LIC CPV Cabo Verde EM ITA Italy AE
MLI Mali LIC JAM Jamaica EM ARE United Arab Emirates EM
SLE Sierra Leone LIC TTO Trinidad and Tobago EM MYS Malaysia EM
SYR Syria EM LKA Sri Lanka EM CYP Cyprus AE
ZWE Zimbabwe LIC RWA Rwanda LIC LVA Latvia AE
MDG Madagascar LIC BTN Bhutan LIC SVN Slovenia AE
SWZ Eswatini EM ECU Ecuador EM CHN China EM
BFA Burkina Faso LIC KEN Kenya LIC PRT Portugal AE
TGO Togo LIC FJI Fiji EM CZE Czech Republic AE
DJI Djibouti LIC BHS Bahamas, The EM ESP Spain AE
GAB Gabon EM KWT Kuwait EM MLT Malta AE
GIN Guinea LIC TUN Tunisia EM LTU Lithuania AE
MDV Maldives EM DOM Dominican Republic EM TWN Taiwan Province of China AE
NER Niger LIC BLR Belarus EM BEL Belgium AE
MMR Myanmar LIC AZE Azerbaijan EM IRL Ireland AE
LAO Lao P.D.R. LIC ARG Argentina EM FRA France AE
NIC Nicaragua LIC MDA Moldova LIC ISL Iceland AE
NGA Nigeria LIC VNM Vietnam LIC HKG Hong Kong SAR AE
MWI Malawi LIC MKD North Macedonia EM NOR Norway AE
CMR Cameroon LIC JOR Jordan EM CAN Canada AE
HND Honduras LIC MNG Mongolia EM AUT Austria AE
VCT St. Vincent and the Grenadines EM COL Colombia EM ISR Israel AE
UZB Uzbekistan LIC PER Peru EM KOR Korea AE
NPL Nepal LIC IND India EM AUS Australia AE
TZA Tanzania LIC ARM Armenia EM GBR United Kingdom AE
UGA Uganda LIC BRN Brunei Darussalam EM JPN Japan AE
LSO Lesotho LIC ZAF South Africa EM LUX Luxembourg AE
GMB Gambia, The LIC PHL Philippines EM SWE Sweden AE
BEN Benin LIC PAN Panama EM DEU Germany AE
CIV Côte d'Ivoire LIC BRA Brazil EM NZL New Zealand AE
TJK Tajikistan LIC MNE Montenegro EM CHE Switzerland AE
PAK Pakistan EM BRB Barbados EM FIN Finland AE
KHM Cambodia LIC UKR Ukraine EM EST Estonia AE
LBR Liberia LIC BHR Bahrain EM NLD Netherlands, The AE
DZA Algeria EM IDN Indonesia EM USA United States AE
ZMB Zambia LIC MUS Mauritius EM DNK Denmark AE
LCA St. Lucia EM ALB Albania EM SGP Singapore AE 
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Annex 2. Additional Information on AI
Occupational Exposure and Potential
Complementarity
Annex Figure 2.1, panel 1, plots the distribution of AI occupational exposure (AIOE) and complementarity for
individual occupations within each major occupational group (that is, 4-digit occupation within each major
group of the International Standard Classification of Occupations [ISCO]-08 classification). As is evident, some
occupational groups are, on average, characterized both by high exposure and high complementarity, such as
professionals, managers, and technicians. Others have both high exposure and low complementarity, such as
clerical workers. Another important observation is that, in general, compared with exposure, the dispersion of
potential complementarity is larger within than across occupational groups, suggesting that the factors that may
determine complementarity are cut across the spectrum of jobs.
Given potential complementarity, θ, a complementarity-adjusted AI occupational exposure (C-AIOE) measure
can be constructed as follows: C-AIOE = AIOE *(1– θ – θMIN)). The adjustment lowers exposure for
occupations with higher values of θ relative to the occupation with the lowest complementarity (θMIN).
Annex Figure 2.1, panel 2 compares AIOE and C-AIOE. For professionals and managers, the average
exposure is much lower after the complementarity adjustment. Meanwhile, clerical occupations, on average,
have the highest complementarity-adjusted exposure, suggesting that they are the most vulnerable to
disruption. Last, for occupational groups with average exposure that was already low, the adjustment does not
substantially change their relative position in the ranking compared with the unadjusted measure.
Annex Figure 2.1. AI Complementarity and Exposure across Major Occupational Groups
1. AIOE and Complementarity (θ) 2. AIOE and C-AIOE
Sources: Felten, Raj, and Seamans (2021); Pizzinelli and others (2023); and IMF staff calculations.
Note: The figure plots the distribution of the values of complementarity θ, unadjusted exposure AIOE (AI occupational exposure), and adjusted exposure
C-AIOE (C for complementarity) across occupations specified by ISCO-08 codes. The boundaries of the whiskers is based on the 1.5 IQR value. The
grouping is at the 1-digit ISCO-08 code level. ISCO = International Standard Classification of Occupations.
*Technicians and associate professionals; **skilled agricultural, forestry, and fishery workers; ***plant and machine operators and assemblers. 
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Annex 3. Methodology for the Worker Transition
Analysis
III.1 Data
To analyze worker reallocation between occupations in Section III, this note uses the panel structure of the UK
Labor Force Survey (LFS) and Brazil’s Pesquisa Nacional por Amostra de Domicílios Contínua (PNADC,
National Continuous Household Sampling Survey). Both surveys have a similar design: households are
interviewed quarterly, and they remain in the sample for five quarters (rolling replacement survey).
Although the PNADC survey identifies households across quarters, it does not identify the number of people
within households. Thus, a matching algorithm must be used to identify individuals across quarters based on
individual characteristics. The note uses the algorithm proposed by Ribas and Soares (2008) and implemented
by Datazoom.
III.2 Constructing Worker Flows
Using the panel data, it is possible to estimate the employment flows and construct the transition matrices
shown in Annex Table 3.1. A transition from unemployment to inactivity (U2N), for example, is defined as
happening when a worker is inactive in the current quarter but was unemployed in the previous quarter.
Similarly, a transition from high-exposure employment to low-exposure employment (HE2LE) is defined as
happening when a worker is employed in an occupation code with exposure above the median in the current
quarter but was employed in an occupation code with exposure below the median in the previous quarter.
An occupational switch, or transition, is defined as happening when a worker reports an occupation code in the
quarter that differs from the occupation code reported in the previous quarter. This includes both job-to-job
transitions (when the worker changes employer) and on-the-job transitions (when the worker switches
occupations but remains with the same employer).
III.3 Wage Dynamics
The UK LFS reports wage data only in the first and final waves of a household’s participation in the survey.
Thus, for the analysis shown in Figure 7, the note considers transitions and wage changes over a period of one
year instead of one quarter. Even though for Brazil wage data are available for all five waves a household
participates in the survey, transitions are still considered over a year so as to keep the methodology consistent
with that used for the UK.
The wage variation is constructed as the variation in the log gross hourly wages between the fifth and first
quarters an individual is in the survey. The following regression specification is run for both countries:
𝑦௜௥௧ ൌ𝛼൅𝛿ଵ𝐽2𝐽௜௥௧ ൅ 𝛿ଶ𝐽2𝐽௜௥௧ ൈ 𝑂𝑆௜௥௧ ൅ 𝛿ଷ𝐸𝑈𝐸௜௥௧
൅෍𝜃௞
௞
𝐶௜௥ሺ௧ିଵሻ ௞ 𝐶௜௥௧
௞ ൅෍෍𝜙௞௝
௞ ௝
𝑂𝑆௜௥௧𝐶௜௥ሺ௧ିଵሻ ௞ 𝐶௜௥௧
௝ 𝑗
൅𝛽𝑋௜௥௧ ൅ 𝛾௧ ൅ 𝜂௥ ൅ 𝜀௜௥௧.
Here, i refers to the individual in the survey, t is the quarter, and r the geographic region, such that 𝛾௧ is a yearquarter fixed effect and 𝜂௥ a region fixed effect. 𝑋௜௥௧ is a matrix of demographic characteristics: age, education, 
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and gender (including age-education interactions, and in the case of Brazil, dummies for informality). J2J is a
dummy variable representing job-to-job transitions, defined as happening when workers have been with their
current employer for less than 12 months in wave 5 of the survey and were employed in wave 1. EUE
represents transition through unemployment, coded when the worker was unemployed in waves 2 through 4.
OS is a dummy for an occupational switch. Last, 𝐶௜௥௧
௞ is a dummy for a worker in exposure category k in period
t.
Thus, the θ௞ coefficient represents the log wage change for “stayers” in category k; that is, those who did not
switch occupations, while ϕ௞௝ is the change for those who changed occupation from exposure category k to
exposure category j. For example, the wage premium relative to stayers plotted in Figure 7 for a worker who
went from HELC to HEHC would be represented as ϕுா௅஼,ுாு஼ െ θுா௅஼.
III.4 Life-Cycle Profiles of Occupational Shares
Figure 6 plots occupational shares in each category, obtained by estimating the following cubic polynomial
regression:
𝐶௜
௞ ൌ 𝛽଴ ൅ 𝛽ଵ𝑎𝑔𝑒௜ ൅ 𝛽ଶ𝑎𝑔𝑒௜
ଶ ൅ 𝛽ଷ𝑎𝑔𝑒௜
ଷ ൅ 𝛿𝑓𝑒𝑚𝑎𝑙𝑒௜ ൅ 𝜀௜,
in which 𝐶௜
௞ is a dummy that indicates whether worker i is in an occupation in exposure category k. The figure
then plots the predicted values 𝐶ప
෢௞ for each age value.
Annex Table 3.1. Quarterly Transition Probabilities across Occupation Types and Labor Market
Statuses for Brazil and the United Kingdom
1. Brazil
(Percent)

2. United Kingdom
(Percent)

Sources: Pesquisa Nacional por Amostra de Domicílios Contínua; UK Labour Force Survey; and IMF staff calculations.
Note: Each cell reports the percentage of workers who transition from the occupation or labor market status listed in the respective row to that listed
in the respective column between two quarters. Each row adds up to 100 percent; that is, the totality of workers in the occupation or labor market
status listed in the respective row in the first quarter. U2N = a transition from unemployment to inactivity; HE2LE = a worker employed in an
occupation code with AI exposure above the median in the current quarter but employed in an occupation code with AI exposure below the median
in the previous quarter.
III.5 AI and Informality
In many emerging market and developing economies, despite high labor informality, AI-induced labor
reallocation is unlikely to affect the size of the formal labor force significantly. Growth in high-exposure, highcomplementarity occupations will likely be in the formal sector, as these roles mostly require skilled, formally
employed workers. Hence, AI’s growth will not necessarily move workers from the informal to the formal sector.
However, workers displaced from high-exposure, low-complementarity occupations may face job loss and
move to informality. Evidence from Brazil, however, indicates a limited risk of such a double blow (Annex
Figure 3.1). A large share of employment in low-exposure occupations is in formal work arrangements (panel
1)—though this finding may not necessarily extend to other emerging market economies. Moreover, most
occupational switches of formal workers have not involved movement into the informal sector (panel 2). 
STAFF DISCUSSION NOTES Gen-AI: Artificial Intelligence and the Future of Work
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Historically, only about 20 percent of workers moving from high-exposure to low-exposure occupations also
entered the informal sector.
Annex Figure 3.1. AI and Informality
1. Share of Employment in Brazil, by Formality
and Exposure Category
(Percent)
2. Probability of a Formal Worker’s Transition to a LowExposure Occupation, by Exposure Category
(Percent)
Sources: Pesquisa Nacional por Amostra de Domicílios Contínua; and IMF staff calculations.
Note: Panel 1 shows the share of employment in total employment according to formality and exposure category. Panel 2 shows the transition
probabilities for formal workers moving to a low-exposure occupation. “From” indicates the exposure category of person’s occupation in the preceding
quarter. The transition probability represents the average share of formal workers in the “from” category who move to a low-exposure occupation.
Blue bars represent the probability of a formal worker moving to a formal job; orange bars represent the probability of a formal worker moving to an
informal job. LE = low exposure.
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Annex 4. Model Details
This annex gives a brief overview of the model’s main elements and considers two extreme scenarios that
illustrate the main channels through which AI adoption affects the economy. The model details are in a paper
by Rockall, Pizzinelli, and Tavares (forthcoming), which combines the models of Drozd, TaschereauDumouchel, and Tavares (2023) and Moll, Rachel, and Restrepo (2022).
IV.1 Main Model Features
Time in the model is viewed as continuous. The final consumption good is produced using intermediate goods
obtained using a continuum of tasks aggregated according to a Cobb-Douglas production function. Tasks can
be produced using labor or capital. Agents are heterogeneous in their skills and ability to invest in capital
markets, leading to variations in their capital endowments. Agents supply labor inelastically across different
sectors and are subject to dissipation shocks. Different sectors pay different wages, and agents who invest in
bonds receive the risk-free rate, whereas those who invest in capital markets receive a higher rate equal to the
return on capital. Agents maximize standard preferences over utility flows from consumption subject to a
budget constraint and a natural debt limit. The heterogeneity in skill types and investment allows the model to
replicate income and wealth inequality.
In the model, there are three main channels through which AI adoption affects the economy. First, labor
displacement arises because tasks performed by labor are carried out by capital, given that technological
progress makes it feasible for AI to perform those tasks. It is assumed that capital is more productive than labor
at performing those tasks, making labor displacement productivity-enhancing. Second, complementarity
reallocates value added, and hence labor demand and income, from workers with less AI complementarity to
workers with high AI complementarity. It is assumed that the complementarity channel does not affect the
overall labor share in the economy. Third, the productivity channel increases the output and wages of workers
with high AI complementarity.
The model’s Cobb-Douglas production function is as follows:
in which 𝜂௭ denotes the importance in value added of the tasks that can be performed by skill z, 𝜓௭ denotes the
productivity of labor for these tasks, and K denotes the aggregate stock of capital in the economy. In this
model, the displacement channel is characterized by changes in 𝛼௭, the complementarity channel by changes
in 𝜂௭, and the productivity channel by changes in 𝜓௭.
IV.2 Additional Scenarios
Two hypothetical scenarios are reported to highlight the impact of the displacement and the complementarity
channels. In the first scenario, the displacement effect affects all workers equally, while complementarity affects
workers according to the data shown in Figure 9, panel 2. In the second scenario, the complementarity channel
is deactivated, and displacement occurs according to the data reported in Figure 9, panel 2. 
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When the displacement effect affects all workers equally (Annex Figure 4.1, panel 1), all workers suffer a loss
of labor income because they experience a decline in the number of tasks they perform. However, workers with
high AI complementarity experience an increase in the demand for tasks that were not displaced at the
expense of workers with low AI complementarity. The combination of these two effects causes workers with
high AI complementarity (who are also high-income workers, as in the data) to accrue most of the gains in
productivity generated by AI adoption. Consequently, AI adoption leads to more significant labor income and
wealth inequality under this scenario.
In contrast, when the AI exposure impact increases with income and there is no complementarity (Annex
Figure 4.1, panel 2), the income gains from adopting AI are higher at the bottom of the income distribution. This
happens because workers at the bottom of the income distribution are less exposed to AI and thus suffer less
task displacement. In contrast, higher-income workers are more exposed and consequently suffer greater task
displacement. As a result, under this scenario, AI adoption leads to lower income inequality since the gains in
capital income are not enough to compensate for the lower gains in labor income at the top caused by task
displacement.
These two scenarios illustrate the importance of how exposure and complementarity are spread across the
income distribution. When exposure is more equally distributed and complementarity is concentrated at the top,
AI adoption may raise income and wealth inequality. When exposure is concentrated at the top of the income
distribution, and complementarity is weak, AI adoption could lead to a decline in income inequality.
Annex Figure 4.1. Change in Total Income by Income Percentile
1. Equally Distributed Exposure and Data-Driven
Complementarity
(Percent)
2. Data-Driven Exposure with No Complementarity
(Percent)
Source: IMF staff calculations.
Note: The plots represent two hypothetical model-based scenarios: (1) equally distributed exposure and data-driven complementarity, and (2) datadriven exposure with no complementarity. For all scenarios, the calibrated change in the capital share is the same: 5.5 percentage points, in line with
the change in the capital share observed in 1980–2014. The plots show the change in total income by income percentile, decomposed into the change
in labor income in blue and the change in capital income in orange. P = percentile.
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Annex 5. AI Preparedness Index
V.1 Indicators
One of the main contributions of this note is the construction of an index—underpinning the analysis in Section
V—that assesses the level of AI
preparedness across countries.
Measuring AI preparedness is
challenging, including because the
institutional requirements for economywide integration of AI are still uncertain.
However, the literature on historical
episodes of technology adoption (see
Keller 2004; Chinn and Fairlie 2007;
Nicoletti, Rueden, and Andrews 2020;
Cirera, Comin, and Cruz 2022) has
identified key determinants that are likely
relevant for AI: digital infrastructure,
human capital, technological innovation,
and legal frameworks. These broad
determinants are supplemented with a set
of indicators expected to be important for
smooth AI adoption. These include
sustained human capital investment,
inclusive STEM [science, technology,
engineering, and mathematics] expertise,
labor and capital mobility within and
across countries, and adaptability of legal
frameworks to new (digital) business
models. The full set of indicators is
summarized in Annex Table 5.1.
The resulting index improves on common
AI readiness indicators in the literature (for example, Oxford Insights 2022) on at least two fronts. First, the
focus is on AI adoption preparedness (rather than invention leadership), allowing comparability of the level of
preparedness across all economies, including low-income countries (where the focus will be more on adopting
than inventing new technologies). Second, the index also crucially incorporates labor market transition
indicators relevant for the AI era, including active labor market (for example, upskilling and skills training) and
social protection. Digital infrastructure and human capital and labor market policies can be considered
"foundational” elements of AI preparedness, because they are prerequisites for its adoption. Innovation and
economic integration and regulation and ethics can be considered “second-generation” elements likely to
maximize the economic impact of AI.
Annex Table 5.1. AI Preparedness Indicators
Source:
Note: Data source for each indicator is shown in square brackets. FI = Fraser Institute;
GNI = gross national income; ILO = International Labour Organization; ITU =
International Telecommunication Union; STEM = science, technology, engineering, and
mathematics; UN = United Nations; UPU = Universal Postal Union; WB = World Bank;
WEF = World Economic Forum.
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V.2 Aggregation and Robustness Checks
Within each of the four aggregate dimensions, the subindicators (𝑥ሻ—for the latest year with available data—
are normalized on a 0–1 scale as follows:
𝑥െ𝑥௠௜௡
𝑥௠௔௫ െ 𝑥௠௜௡
Each aggregate dimension (digital infrastructure,
human capital and labor market policies,
digital innovation and economic integration,
regulation and ethics) is the simple average of its
normalized subcomponents. The AI Preparedness
Index is then derived as the simple average of the
four aggregate dimensions. The index is computed
for 32 advanced economies, 56 emerging market
economies, and 37 low-income countries. Annex
Figure 5.1 summarizes the level of AI
preparedness and its main components for
selected economies.
Section V shows that the index’s components are
correlated with information and communications
technology employment, corroborating their
relevance. In addition, the strength of these
correlations conditional on development levels
makes intuitive sense.
Employing simple averages in aggregating the
index has at least two shortcomings.20 First, the
equal weighting inherently risks undervaluing key
components and overemphasizing minor ones,
obscuring vital weaknesses or strengths, by
spreading their impact across the aggregate index. Second, the use of simple averages is sensitive to outliers
and extreme values.
As a robustness check, we employ principal component analysis (PCA) in aggregating the index. For each
aggregate dimension, the first principal component (PC) of subindicators is extracted, normalized between 0
and 1, and the index is then computed as the sum of these normalized PCs. The results based on the PCA are
indistinguishable from those obtained with simple averaging.

20 Other aggregation methods have their own strengths, but they also come with drawbacks in this context. For example, a constant
elasticity of substitution (CES) aggregation, which would assume imperfect substitutability among the index’s components, could
suggest that a deficit in regulatory frameworks could be imperfectly substituted by, say, strong performance in innovation.
Annex Figure 5.1. Cross-Country AI Preparedness
Dimensions: Selected Countries
Source:
Note: The figure shows the contribution of digital infrastructure, innovation
and integration, human capital and policies, and regulation and ethics to AI
preparedness by country. The length of the bar indicates AI preparedness.
Highlighted bars denote the country group average. AEs = advanced
economies; EMs = emerging market economies; LICs = low-income
countries. Country names use International Organization for
Standardization (ISO) country codes. 
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References
Acemoglu, D., and P. Restrepo. 2018. “Artificial Intelligence, Automation, and Work.” In The Economics of
Artificial Intelligence: An Agenda, edited by A. Agrawal, J. Gans, and A. Goldfarb, 197–236. Chicago:
University of Chicago Press.
Acemoglu, D., and P. Restrepo. 2022. “Tasks, Automation, and the Rise in US Wage Inequality.” Econometrica
90 (5): 1973–2016.
Albanesi, S., A. D. da Silva, J. F. Jimeno, A. Lamo, and A. Wabitsch. 2023. “New Technologies and Jobs in
Europe.” CEPR Discussion Paper DP18220, Centre for Economic Policy Research, London, UK.
Alonso, C., A. Berg, S. Kothari, C. Papageorgiou, and S. Rehman. 2022. “Will the AI Revolution Cause a Great
Divergence?” Journal of Monetary Economics 127: 18–37.
Amaglobeli, D,. R. de Mooij, A. Mengistu, M. Moszoro, M. Nose, S. Nunhuck, S. Pattanayak, L. Rivero del
Paso, F. Solomon, R. Sparkman, H. Tourpe, and G. Uña. 2023. “Transforming Public Finance through
GovTech.” IMF Staff Discussion Note 2023/004, International Monetary Fund, Washington, DC.
Autor, D. H., and D. Dorn. 2013. “The Growth of Low-Skill Service Jobs and the Polarization of the US Labor
Market.” American Economic Review 103 (5): 1553–597.
Autor, D. H., F. Levy, and R. J. Murnane. 2003. “The Skill Content of Recent Technological Change: An
Empirical Exploration.” Quarterly Journal of Economics 118 (4): 1279–333.
Babina, T., A. Fedyk, A. He, and J. Hodson. Forthcoming. “Artificial Intelligence, Firm Growth, and Product
Innovation.” Journal of Financial Economics.
Bank of America. 2023. “Cybersecurity Report: Landscape, Trends & What Comes Next.” Bank of America
Global Research, New York.
Bartel, A., C. Ichniowski, and K. Shaw. 2007. “How Does Information Technology Affect Productivity? PlantLevel Comparisons of Product Innovation, Process Improvement, and Worker Skills*.” Quarterly
Journal of Economics 122 (4): 1721–758.
Berg, A., L. Bounader, N. Gueorguiev, H. Miyamoto, K. Moriyama, R. Nakatani, and L. F. Zanna. 2021. “For the
Benefit of All: Fiscal Policies and Equity-Efficiency Trade-offs in the Age of Automation.” IMF Working
Paper 2021/187, International Monetary Fund, Washington, DC.
Bergholt, D., F. Furlanetto, and N. Maffei-Faccioli. 2022. "The Decline of the Labor Share: New Empirical
Evidence." American Economic Journal: Macroeconomics 14 (3): 163–98.
Bloom, N., M. Draca, and J. Van Reenen. 2015. “Trade Induced Technical Change? The Impact of Chinese
Imports on Innovation, IT and Productivity.” Review of Economic Studies 83 (1): 87–117.
Braxton, J. Carter, and B. Taska. 2023. "Technological Change and the Consequences of Job Loss." American
Economic Review 113 (2): 279–316.
Brynjolfsson, E., L. Danielle, and L. R. Raymond. 2023. "Generative AI at Work." NBER Working Paper 31161,
National Bureau of Economic Research, Cambridge, MA. 
STAFF DISCUSSION NOTES Gen-AI: Artificial Intelligence and the Future of Work
INTERNATIONAL MONETARY FUND 37
Boukherouaa, E., G. Shabsigh, K. AlAjmi, J. Deodoro, A. Farias, E. S. Iskender, A. T. Mirestean, and R.
Ravikumar. 2021. “Powering the Digital Economy: Opportunities and Risks of Artificial Intelligence in
Finance.” IMF Departmental Paper 2021/024, International Monetary Fund, Washington, DC.
Briggs, J., and D. Kodnani. 2023. “The Potentially Large Effects of Artificial Intelligence on Economic Growth.”
Goldman Sachs - Global Economics Analyst, New York.
Carriere-Swallow, Y., and V. Haksar. 2019. "The Economics and Implications of Data: An Integrated
Perspective," IMF Departmental Paper 2019/013, International Monetary Fund, Washington, DC.
Cazzaniga, M., C. Pizzinelli, E. Rockall, and M. M. Tavares. Forthcoming. “Exposure to Artificial Intelligence
and Occupational Mobility: A Cross-Country Analysis.” IMF Working Paper, International Monetary
Fund, Washington, DC.
Chinn, M. D., and R. W. Fairlie. 2007. “The Determinants of the Global Digital Divide: A Cross-Country
Analysis of Computer and Internet Penetration.” Oxford Economic Papers 59 (1): 16–44.
Cirera, X., D. Comin, and M. Cruz. 2022. "Bridging the Technological Divide: Technology Adoption by Firms in
Developing Countries." World Bank, Washington, DC.
Colombo, E., F. Mercorio, and M. Mezzanzanica. 2019, "AI Meets Labor Market: Exploring the Link between
Automation and Skills." Information Economics and Policy 47: 27–37.
Cortes, G. M., N. Jaimovich, and H. E. Siu. 2017. "Disappearing Routine Jobs: Who, How, and Why?" Journal
of Monetary Economics 91: 69–87.
Cortes, G. M., N. Jaimovich, C. J. Nekarda, and H. E. Siu. 2020. "The Dynamics of Disappearing Routine Jobs:
A Flows Approach." Labour Economics 65: 101823.
Dabla-Norris, E., C. Pizzinelli, and J. Rappaport. 2023. "Job Polarization and the Declining Wages of Young
Female Workers in the United Kingdom." Oxford Bulletin of Economics and Statistics 85 (6): 1185-
1210.
Dao, M. C., D. Mitali, and Z. Koczan. 2019. "Why Is Labour Receiving a Smaller Share of Global
Income?" Economic Policy 34 (100): 723–59.
Das, M., and B. Hilgenstock. 2022. "The Exposure to Routinization: Labor Market Implications for Developed
and Developing Economies." Structural Change and Economic Dynamics 60 (C): 99–113.
Drozd, L. A., M. Taschereau-Dumouchel, and M. M. Tavares. 2022. "Understanding Growth through
Automation." Research Department, Federal Reserve Bank of Philadelphia, Philadelphia, PA.
Eloundou, T., S. Manning, P. Mishkin, and D. Rock. 2023. “GPTs are GPTs: An Early Look at the Labor Market
Impact Potential of Large Language Models.” arXiv.org working paper.
Financial Conduct Authority (FCA). 2022. “Machine Learning in UK Financial Services.” Bank of England and
Financial Conduct Authority, London, UK.
Felten, E., M. Raj, and R. Seamans. 2021. "Occupational, Industry, and Geographic Exposure to Artificial
Intelligence: A Novel Dataset and Its Potential Uses." Strategic Management Journal 42 (12): 2195–
217.
Felten, E., M. Raj, and R. Seamans. 2023. "How Will Language Modelers Like ChatGPT Affect Occupations
and Industries?" arXiv.org working paper. 
STAFF DISCUSSION NOTES Gen-AI: Artificial Intelligence and the Future of Work
INTERNATIONAL MONETARY FUND 38
Gmyrek, P., J. Berg, and D. Bescond. 2023. Generative AI and Jobs: A Global Analysis of Potential Effects on
Job Quantity and Quality. ILO Working Paper 96. International Labour Organization, Geneva,
Switzerland.
International Finance Corporation (IFC). 2020. “Artificial Intelligence in Emerging Markets: Opportunities,
Trends and Emerging Business Models.” International Finance Corporation. World Bank, Washington,
DC.
Ilzetzki, E., and S. Jain. 2023. The Impact of Artificial Intelligence on Growth and Employment. VoxEU.org,
June 20.
Haksar, V., Y. Carriere-Swallow, E. Islam, A. Giddings, K. Kao, E. Kopp, and G. Quiros-Romero. 2021.
"Toward a Global Approach to Data in the Digital Age," IMF Staff Discussion Note 2021/005,
International Monetary Fund, Washington, DC.
International Monetary Fund (IMF). 2017. World Economic Outlook: Gaining Momentum? Chapter 3.
International Monetary Fund, Washington, DC, April.
International Monetary Fund (IMF). 2023. World Economic Outlook: A Rocky Recovery, Chapter 2.
International Monetary Fund, Washington, DC, April.
Jamilov, R., H. Rey, and A. Tahoun. 2023. "The Anatomy of Cyber Risk." NBER Working Paper 28906,
National Bureau of Economic Research, Cambridge, MA.
Kambourov, G., and I. Manovskii. 2009. "Occupational Mobility and Wage Inequality." Review of Economic
Studies 50: 731–59.
Keller, W. “International Technology Diffusion.” 2004. Journal of Economic Literature 42 (3): 752–82.
Klinova, K., and A. Korinek. 2021. "AI and Shared Prosperity." In Proceedings of the 2021 AAAI/ACM
Conference on AI, Ethics, and Society, 645–51.
Moll, B., L. Rachel, and P. Restrepo. 2022. "Uneven Growth: Automation's Impact on Income and Wealth
Inequality." Econometrica 90 (6): 2645–683.
Monsueto, S. E., A. Moreira Cunha, and J. da Silva Bichara. 2014. "Occupational Mobility and Income
Differentials: The experience of Brazil between 2002 and 2010." Cepal Review 113: 139–55.
Moscarini, G., and F. G. Vella. 2008. “Occupational Mobility and the Business Cycle.” NBER Working Paper
13819, National Bureau of Economic Research, Cambridge, MA.
Nicoletti, G., C. V. Rueden, and D. Andrews. 2020. “Digital Technology Diffusion: A Matter of Capabilities,
Incentives or Both?” European Economic Review 128: 103513.
Organisation for Economic Co-operation and Development (OECD). 2023. “OECD Employment Outlook 2023:
Artificial Intelligence and the Labour Market.” Paris, France.
Oxford Insights. 2022. “Government AI Readiness Index.” Malvern, UK.
Pizzinelli, C., A. Panton, M. M. Tavares, M. Cazzaniga, and L. Li. 2023. "Labor Market Exposure to AI: CrossCountry Differences and Distributional Implications." IMF Working Paper 2023/216, International
Monetary Fund, Washington, DC.
Ribas, R. P., and S. S. D. Soares. 2008. “The IBGE Monthly Employment Survey (PME) Panel.” Discussion
Paper 1348, Institute of Applied Economic Research, Brasília, Brazil. 
STAFF DISCUSSION NOTES Gen-AI: Artificial Intelligence and the Future of Work
INTERNATIONAL MONETARY FUND 39
Rockall, E., C. Pizzinelli, and M. Mendes Tavares. Forthcoming. "Artificial Intelligence Adoption and Inequality."
IMF Working Paper, International Monetary Fund, Washington, DC.
Sahay, R., M. Čihák, P. N’Diaye, A. Barajas, S. Mitra, A. Kyobe, Y. N. Mooi, and S. R. Yousefi. 2017. "Financial
Inclusion: Can It Meet Multiple Macroeconomic Goals?" IMF Staff Discussion Note 2015/017,
International Monetary Fund, Washington, DC.
Sahay, R., and M. Čihák. 2020. "Finance and Inequality." IMF Staff Discussion Note 2020/001, International
Monetary Fund, Washington, DC.
Sahay, R., U. Eriksson von Allmen, A. Lahreche, P. Khera, S. Ogawa, M. Bazarbash, and K. Beaton. 2020.
"The Promise of Fintech: Financial Inclusion in the Post COVID-19 Era." IMF Departmental Paper
2020/009, International Monetary Fund, Washington, DC.
Shabsigh, G., and E. B. Boukherouaa. 2023. “Generative Artificial Intelligence in Finance.” Fintech Note
2023/006, International Monetary Fund, Washington, DC.
United Nations Educational, Scientific and Cultural Organization (UNESCO). 2021. “AI and Education:
Guidance for Policy-Makers.” Paris, France.
US Agency for International Development (USAID). 2019. “Artificial Intelligence in Global Health: Defining a
Collective Path Forward.” Washington, DC.
Wahl, B., A. Cossy-Gantner, S. Germann, and N. R. Schwalbe. 2018. "Artificial Intelligence (AI) and Global
Health: How Can AI Contribute to Health in Resource-Poor Settings?" BMJ Global Health 3 (4):
e000798.
Webb, M. 2020. “The Impact of Artificial Intelligence on the Labor Market.” Stanford University Working Paper,
Stanford, CA.
Wootton, C. W., and B. E. Kemmerer. 2007. "The Emergence of Mechanical Accounting in the US, 1880–
1930." Accounting Historians Journal 34 (1): 91–124.
Yashiro, N., T. Kyyrä, H. Hwang, and J. Tuomala. 2022. “Technology, Labour Market Institutions and Early
Retirement.” Economic Policy 37 (112): 811–49. 